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Review of Capacity Improvement Strategies for Bus transit service

MUKTI ADVANI AND GEETAM TIWARI*

Abstract

Transportation demands in urban areas continue to increase rapidly as a result of both population growth and changes in travel patterns. In the era of environment concerns and limited space available in cities, transport planners have to provide a system, which can ensure safe and clean mobility to all city residents. This requires planning a system, which is affordable, reliable and efficient from the users’ as well as operator’s perspectives. A road based bus system offers an opportunity for creating a system capable of meeting multiple needs of users and operators. This paper presents a critical review of recent planning methodologies and selected decision support systems for optimizing urban bus transport services. These methodologies offer incremental improvements in bus system to meet the capacity requirements of different size cities. It is imperative that bus systems are planned such that they satisfy the requirements of users as well as service providers within the limited resource constraints. A flexible, comfortable, easily available and reliable bus service is expected to shift people from private vehicles to public transport. This paper presents a review of strategies which can be employed to satisfy public transport demands of different city sizes.

Keywords

Bus, Optimization, Transfer, Synchronization.

* Ms. Mukti Advani is Research Scholar and Geetam Tiwari is Associate Professor, Transportation Research & Injury Prevention Programme (TRIPP), Indian Institute of Technology, Delhi.

INDIAN JOURNAL OF TRANSPORT MANAGEMENT

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1. INTRODUCTION

Indian cities, of all sizes, face a crisis of urban transport. Despite investments in road infrastructure, and plans for landuse and transport development, all cities face the ever increasing problems of congestion, traffic accidents, air, and noise pollution. Large cities are facing a rapid growth of personal vehicles (two wheelers and cars) and in medium and small cities different forms of intermediate public transport provided by the informal sector are struggling to meet the mobility demands of city residents.

Public transport is a critical element of urban transport system. A great

emphasis needs to be given to the public transport system because it offers the most efficient utilization of limited resources – energy and land. At present, a range of public transport options are available for different city sizes. A careful analysis is required to select the most appropriate technology for a given city size. Rail based systems have often been recommended based on their capacity to move large number of people (> 30,000 passengers/phpd). However, they are controversial options of public transport because of the large financial burden they can impose on city budgets. Also, the existing rail based systems have shown low ridership, low capacity utilization and high operating subsidies.

Bus transport is the most desirable and sustainable system from societal

perspective. A well planned bus system can provide a high level of mobility to a large section of the population with least cost. However, a poorly planned system causes inconvenience to the users, loses ridership, encourages use of private vehicles and imposes financial burden on the operator.

A flexible, comfortable, easily available and reliable bus service may

encourage shift from private vehicles to public transport. Since travel demand varies over time and space, public transport systems often have under utilized capacity at non peak hours and high load factor in peak hours. The objective of an efficient system is to meet the diverse demands and minimize operator’s loss. This requires that the optimizing, routing, scheduling and synchronizing problems are given special attention, while designing an efficient bus system.

2. Urban Travel Demand

There are 4,000 cities and towns in India including cities having population less than 1 million to more than 9 millions. As shown in Table 1, 147 cities have a population of less than 1 lakh, and 177 cities have a population between 1 to 5 lakh. About 28 cities have a population of 5 to 10 lakh, 6 cities


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with population of 10 to 20 lakh, 3 cities with population of 50 to 100 lakh and 3 cities have a population of more than 100 lakh.

Table 1: Indian cities and population

Population No. Of Cities

< 1 147 1-5 lakh 177 5-10 lakh 28 10-20 lakh 20 20-50 lakh 6 50-100 lakh 3

1 crore 3 (Source: Census of India, 2001)

There is a large variation in the travel demand met by public transport

system, intermediate public transport system or private modes in these cities. Existing intra city trips by bus in various cities of India are as shown in Table 2.

Table 2 shows a large variation in the share of bus trips even amongst cities of similar size. Clearly there are factors other than the population size of the city that are responsible for this large variation. Spatial and temporal availability, reliability, comfort and affordability are some of the important parameters that influence the usage pattern of bus services. If an extensive bus network, having high frequency, is available to commuters at affordable prices (often less than marginal cost of using a two wheeler), it is likely to attract large number of commuters. However, this may result in over supply and poor utilization factor leading to large gaps between cost of providing the system and revenue generation. Therefore planning strategies that can meet the varying demand efficiently are required.

In some small cities, bus service is only a single route across the city.

Often intercity buses run by State Transport Undertaking are used for city operations. Scheduling of these services is not based on demand analysis. The second level of bus services includes more than one route; however, scheduling is based on the observation that the morning and evening peak requirements are more than the rest of the day. Many metropolitan cities have public owned transport companies for example Bangalore, Delhi, Mumbai, and Pune. Services provided by these companies are based on demand analysis. However improvements in reliability, speeds, availability, cost reduction that can be brought out by improved scheduling, feeder system, changes in road design, bus stop location, and signal system have not been explored. Bus system is capable of


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carrying 100 persons in each bus to about 40,000 persons per hour depending on the strategies that used for designing the system.

Table 2: Existing intra city trips made by bus and city population.

City Name Percentage of trips by Bus

Population in Millions*1

Population in Millions*2

Panipat 1 0.19 0.21 Ludhiana 1 1.04 1.39 Nagpur 5 1.66 2.05 Udaipur 6 0.31 0.6 Varansi 9 1.0 1.1 Agartala 15 0.1 0.12 Vadodara 15 1.13 1.3 Kanpur 18 2.03 2.53

Dhanbad 27 0.82 1.06 Ahmedabad 27 3.31 3.45

Tirupur 28 0.31 0.6 Pune 29 2.49 2.54

Bhopal 30 1.06 1.43 Vijaywada 34 0.85 1.01 Rourkela 36 0.40 1.57 Guwahati 47 0.58 0.8

Visakhapatanam 47 1.06 1.32 Madras 49 - 6.40

Hubli-Dharwad 50 0.65 0.6 Cochin 54.4 - 1.66 Delhi 62.4 - 13.78

Guruvayur 64 0.12 0.27 Shimla 86 0.11 0.62

Calcutta 89 11.0 14.0 (Source:*1 RITES, 1998, *2 Census of India 2001)


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3. City size and Public Transport Demand

The demand for public transport is dependent on both physical and socio economic characteristics of urban areas. Availability of road infrastructure, geographical spread of the city, mixed landuse patterns, population density, and income distribution are some of the important characteristics that influence public transport demand. As these characteristics change with time, the public transport demand also changes. The different level of public transport demand can be broadly divided in four major levels for bus transport planning:

3.1 Public Transport Demand Level 1

This level considers existing small towns that generally do not have more than one or two major routes. This includes cities having population between 1 lakh to 5 lakhs. Table 3 shows that cities of this population range, have 95% network with travel demand of less than 4000 and 5% network has demand between 4000 and 8000 person per hour per direction traffic (phpdt). Public Transport service for these cities can be improved by increasing bus frequencies on these routes. Maintaining a timetable prepared on the basis of demand generally helps to improve the reliability of public transit service. Since, in small cities, average trip length generally remains short, therefore, use of two wheelers is very convenient. In this case, then, the public transit has to compete with cost and reliability of two wheelers in order to attract passengers.

Table 3: Distribution of Mass transport network by trip loads in PHPDT1 (%)

Distribution of Mass transport network by trip loads in PHPDT1 (%)

Proportion of transport network length in city of population size PHPDT

1-5 6 lakh 10 lakh 35 lakh 85 lakh

<4000 95 80 60 40 34

4000-8000 5 18 20 20 26

8000-12000 - 2 12 9 16

12000-20000 - - 8 9 10

20000-30000 - - - 15 7

30000-40000 - - - 5 4

More than 4000 - - - - 3

(Source: RITES,1999) 1 (PHPDT – Per Hour Per Direction Traffic)


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The second level comprises of cities that require more than just one or two routes. This includes cities with populations of 5 lakhs to 10 lakhs. Table 3 shows that in cities of this range, the percentage of network with travel demand less than 4000 phpdt varies from 60 to 80% and it is 28 to 20 % for demand between 4000 and 8000 phpdt, 2 to 12% for demand of 12000 to 20000 phpdt, 0 to 8% for more than 20000 phpdt. These cities generally have a network of routes spread out over the city. Direct routes may not be available to all the passengers and at least one change over may be required to reach the final destination. To improve public transit service in such cities it is important to have coordination between different routes so that wait time at change-over nodes may be reduced. This requires a coordinated and reliable timetable, safe and comfortable bus stops, and frequency based on peak and off peak hour demand in all directions. The system can be planned for feeder trips by bicycles or cycle-rickshaws to improve the catchment area of the public transit system. 3.3 Public Transport Demand Level 3

There are at least 30 cities in India with population ranging between 1 million and 5 million. In cities having population 3.5 million approximately 40% of network has demand less than 4000 phpdt, 20% with demand between 4000 to 8000 phpdt, 9% with demand between 8000 to 12000 phpdt, 9% with demand between 12000 to 20000 phpdt, 15% with demand 20000-30000 phpdt and 5% has demand between 30000 to 40000 phpdt ( Table 3). These cities have an extensive road network and the public transit service is provided by state run corporation. To cope up with demand, the planning process needs separate study for each area/route and identification of major activity centers. Separate lanes for bus and a bus stop design that reduces dwell time for buses can provide 15000-20000 phpdt in bus system (Meirelles 2004). This level presumes the inclusion of all factors taken in consideration in the planning of transit service of level 1 and 2 categories.


Cities having more than 5 million populations lie in this category. Table 3 shows that in a city having 8.5 million population, approximately 34% of network has demand less than 4000 phpdt, 26% has demand between 4000 to 8000 phpdt, 16% has demand between 8000 to 12000 phpdt, 10% has demand between 12000 to 20000 phpdt, 7% has demand 20000-30000 phpdt and 7% has demand more than 30000 phpdt on Bus Rapid Transit (BRT) or High Capacity Bus System (HCBS). BRT systems, with priority signal design, separate middle lane for buses, specially designed articulated buses, specially designed bus stops, bus fleet


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management and passenger information systems, have been implemented successfully in few cities to serve this kind of demand. (GTZ, 2002 )

Systems such as Quito (Ecuador) and Curitiba (Brazil) that utilize just one

lane in each direction reach capacities of approximately 10000 phpdt. However, Porto Algre (Brazil) also has only one lane availability in each direction but reaches capacities over 20000 phpdt through the clever use of multiple stopping bays and the platooning of vehicle movements. Curitiba’s collective transportation system is built on a backbone of interesting busways, supported by a large network of feeder buses. Another important aspect of the bus transit system in Curitiba is its integrated tariff, which allows trips and transfers throughout the system for a single fare.

4. Capacity Improvement Strategies for Bus system Table 4 shows capacity of bus system in different cities. Table 4: Bus system capacity and population of different cities of world.

City Bus system capacity (passengers/hour/direction)

Population

Belo horizontre 26800 2.2 million Campinas 10700 0.9 million Curitiba 11100 1.6 million Goiania 7400 1.1 million

Porto alegre 24100 1.3 million Recife 26600 1.4 million

Sao Paulo 45900 10.9 million (Source: Meirelles, 2000)

Primarily two classes of strategies have been employed to improve bus

system capacities.

1. Operational Strategies 2. Infrastructural modifications

4.1 Operational Strategies

Optimizing, routing, scheduling and synchronizing issues are the major components of activities related to the transportation of goods and persons traveling from a point of origin to a point of destination. The goal is to provide the best service to the customer at minimum cost to the producer. These two objectives are often contradictory, that is better service is more costly therefore the transportation enterprise must optimize its resources to find an economical


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way to distribute its goods or services while maintaining the goals and constraints of its marketing strategy.

Figure 1 shows that how each factor changes performance of public

transport service and finally how it increases its share in people movement.

Figure 1: Bus Priority Strategy

(Source: http://www.buspriority.org/performance.htm)


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Solving a routing and scheduling problem involves many compromises within a set of conflicting goals and constraints, like using the main roads other than the shortest route, and giving a better average service rather than a better overall service. For a bus transit service, it is always preferable to expand its catchment area as much as possible. But as demand varies with time it is not feasible to provide best services at all the time. Low demand at non-peak hours and high operators’ costs result in excess capacity if service levels are not changed. All major destination points should be covered in the route. Therefore service should be provided in a way, which satisfies the major requirements without increasing operating cost. This process usually prompts more modifications, which in turn must be evaluated. Recent publications on operational strategies of bus transit service have addressed the following areas: 1) Route optimization

2) Transfer optimization 3) Feeder buses 4) Timetable preparation Figure 2 shows different variables used in optimization process in each

area. A totally synchronized bus transit service needs to address all these issues. Figure 2: Different variables included for total synchronized planning of

bus transit service.


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4.1.1 Route optimization

It is generally recognized that optimization procedures in transportation can reduce delivery cost from 10% to 20%. Higher savings are expected when enterprises only provide transportation i.e. bus service authorities, postal services, or couriers. (Kearney 1980). A large number of algorithms and methods have been developed for solving optimized routing and scheduling problems but many aspects and constraints have yet to be taken into account.

Steven (2003) has developed Genetic algorithm (GA) to optimize a bus

transit system serving an irregularly shaped area with a grid street network. The objective is to minimize the total cost and it is subject to the realistic demand distribution and street pattern. This paper develops a GA to optimize a bus system including the number of routes, their locations and operating headways. Intersection delays and realistic street patterns are considered with an assumption that buses can stop anywhere along the bus line when there is a request from boarding or alighting passengers. Thus, the location of bus stops can be neglected. Steven concludes that relocating bus routes and optimizing corresponding headways may reduce the operating cost as well as improve the transit system’s accessibility.

The bus route selection problem discussed in this study is a combinational

optimization problem. It is worth noting that the proposed GA can be efficiently applied to optimize bus route locations for any bus system operated in an area with a grid street network. The integration of demand data and GIS street network information improves the quality and quantity of data source, as well as the efficiency in generating input data from the GIS database. A quick and an adequate solution to such a dynamic bus route planning problem can reduce transit operating cost, improve the transit level of service and attract people using public transportation systems. Application of route optimization strategies can improve capacity and performance of bus system for level 2, 3 and 4 cities as described in the earlier section. 4.1.2 Transfer optimization

Often dispersed origins and destinations require optimal transfer points. It is very expensive to provide direct bus for each pair of origin and destination; therefore transfers should be scheduled in a way, which reduces the waiting time at the transfer nodes. Users prefer to reduce the number of transfers as much as possible because each transfer demands some physical effort. Advani & Tiwari (2004) discuss the accessibility index, which shows that how each transfer affects the accessibility of any transport services.


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For minimizing the waiting time at transfer nodes, a well-synchronized timetable is required. One of the major tasks of any transportation service is to create timetables for the bus routes of a given bus network. In this task, the scheduler has to take care of satisfaction and convenience of the users by providing maximum synchronization. For this few algorithms have been formulated as the efficiency depends on the total waiting time at the transfer nodes. (Ceder et al, 2001)

Kalaga and Haripersad (1999) have shown that, in most of the transit

networks it is not always worthwhile to provide a direct route when there is no adequate demand. Thus some users may need to make transfer when there are no direct routes. This transfer involves not only waiting time but in general causes inconvenience to the users particularly during bad weather conditions. A model is presented which aims to reduce passenger discomfort (waiting time) due to transfers. The algorithm developed was applied to the bus transit system in Durban, under the control of Durban Transport. The results were compared with the actual bus schedules, and the efficiency of the algorithm developed was tested. It was observed that the waiting time for all connector-trips was reduced by 5 minutes. The solution has represented an efficient way to reduce passenger inconvenience and to improve the level of service of the bus network. The limitation of the method presented is in the implicit assumption of a constant headway on every line. Arrival times were assumed following a uniform distribution. This is not always the case and for arrival times following other distributions, the relevant changes can be made accordingly.

Inconvenience to the passenger who needs transfers should be minimized.

Bus arrivals and departure timings should be scheduled in a manner, which minimizes the delay or waiting time.

Adamski and Bryniarska (1996) have shown that transfer involves certain

inconveniences connected with discomfort of boarding a new vehicle, negative perception of waiting for arrival new vehicle and existence of some delay during a trip. The elimination of these inconveniences by schedule synchronization to provide an attractive service level with easy access and transfer possibilities is a challenging problem in timetable construction.

This procedure of transfer optimization starts with an initial solution and

looks for improvements by changing the offset time of one link at a time, until no further improvement can be obtained. In level 3 and 4, bus system is required to serve a layer area, covered by multiple routes. Often commuters have to depend on many more than one route to reach their final destination. Therefore use of strategies that can optimize transfer time becomes important.


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4.1.3 Feeder buses

Buses also play an important role as feeder services to higher capacity systems like bus rapid transit system and rail based systems. Metro rail systems are high capacity systems. To utilize this capacity, the catchment area of metro rail is expanded by using buses as feeder to the high capacity corridor. Buses have also been used, as a feeder system to the rail link to shopping centers and recreational areas. The service is expected to reduce the use of private cars and thus reduce car congestion in the destination corridor.

Cardone et al. (2003) have developed, a model for optimizing a feeder bus

service connecting a transfer rail station to a recreational facility. In this, a total cost function, including supplier and user cost, is developed. Users’ cost includes transfer and slack time costs incurred by local passengers. The decision variables, including route choice, headway, fleet size, vehicle size and slack time are optimized by minimizing the total cost function. The objective of this study is to develop a model that minimizes the total cost of a feeder bus service to Sandy Hook park, subject to site-specific constraints including route limitations, coordinating vehicle schedules, bus availability, service capacity and budget. The cost comprised of supplier and user costs. The decision variables in determining the total cost are route choice, headway, fleet size, vehicle size and slack time.

The model formulation developed by Cardone et al. (2003) in a project

allows transit companies to offer a feeder bus service from one inter-modal transit stop to a recreational facility. The model optimizes number of vehicles; vehicle size, headway and slack time to provide additional benefits i.e. less congestion, less fuel consumption, less air pollution. These benefits alone will encourage officials of other points of interest to use the developed model to reduce congestion to their sites.

Zhan (1998) has shown that the objective function is the net benefit

represented by benefits that accrue to the users for lower average waiting times minus operator’s cost not covered by fares. A numerical procedure to solve the problem is provided. The criterion often adopted for urban bus service, which aims at a given line capacity at minimal cost to the operator, has led to a gradual increase in bus size, mainly to offset the high incidence of personnel costs in operating costs. This approach ignores the user benefit possible from smaller buses, which can provide the same line capacity with a more frequent service. Further advantages often claimed for smaller buses are higher average speed, from which both users and operator can benefit, and lower unit capital and operating costs.

Zhan (1998) has considered both the cases of random and regular bus

arrivals. The former is likely to occur in mixed-traffic, heavily congested


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corridors. In the latter, schedule coordination needs to be maintained. For random bus arrivals, frequencies of the buses are set to a multiple of those of the full-length line and relative offset are added to the set of design variables. In the case of regular bus arrivals, scheduling mode and relative offset differentiated by operation period, while the settings of the other variables are unchanged over the whole operation time. 4.1.4 Total synchronization

Total synchronization is the most difficult task of transit schedulers. It is a combined strategy for the required frequency, efficient assignment of trips to a single bus chain and synchronization of certain arrivals. For a good and synchronized service, optimization of routes, optimization of transfers, an effective time scheduling and availability of effective feeder services are required.

Ceder et al. (2000) argue that usually a global approach from the user's

perspective considers the minimization of travel and waiting (and possibly walking) times. It is a transit network design approach, which accounts for origin-destination (O–D) data. However, they assume an existing transit network of routes with certain passenger demand by time of day, focusing on maximal synchronization. The maximal synchronization is an important objective from both the operator's and the user's perspectives, involving as it does creating timetables that will maximize the number of simultaneous arrivals of buses at the connection (transfer) nodes. There is a trade-off between the elimination of transfer and waiting time and the efficiency of the bus route network from the operating cost perspective. In order to allow for an adequate level-of-service, the schedulers face the synchronization task to ensure maximal smooth transfers involving switching passengers from one route to another without waiting time. This task is extensive because it includes minimizing waiting time for those passengers who require connections. By doing so, the scheduler creates a more attractive transit system that generates the opportunity for increasing the number of riders.

Ceder et al. (2000) also present a model, which enables transit schedulers

to set restrictions on the headways for each route, to introduce different frequencies for every route, and to apply other constraints. The objective function is to maximize the number of simultaneous bus arrivals in the network. The departure times are set in such a way as to achieve this goal. It is worth mentioning that the model presented is also capable, with a small extension, to assign different weights on each different simultaneous arrival (two different lines or time periods). That is, if the scheduler wishes to provide different importance levels for each synchronization situation, he may then introduce the weights, and the objective function will change to maximize the sum of all weights.


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4.1.5 Timetable preparation

Transit timetable is a predominant bridge between the operators and passengers. Therefore, attention should be given to construction of timetables to provide a reliable bus service to the fluctuating passenger demand.

Ceder (2001) shows that a bus timetable is perhaps the main factor in

service reliability. The assumption that passengers will adjust to given timetables instead of adjusting timetables to passenger demand is a main cause of unreliable service. Ceder (2001) proposes three different procedures for improving synchronization of passenger demand with a given timetable while attempting to minimize the number of departures, a reduction in the number of buses at the same time improving reliability and comfort in bus service. Figure 3 schematically presents the three procedures.

Figure 3: Three different procedures for better matching passenger demand

with a given timetable

(Source: Ceder, A., 2001)

• Average hourly (j) max load

• Hourly (j)

desired occupancy (dj)

• Hourly (j)

minimum frequency (Fmj)

Procedure 2: Even average loads on individual buses at the Lj points

Departure times arranged for even average loads on all buses at Lj points

Procedure 3: Even average loads on individual buses at their individual maximum load points

Departure times arranged for even average loads on all buses at their individual maximum points

Departure times with evenly spaced headways for each hour

• Individual bus loads across the entire bus route

• Dj • Fmj

• Individual bus loads at the Lj point

• dj • Fmj

Procedure 1: Even headways with smooth transitions


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Procedure 1 has departure times with evenly spaced headways and a

smooth transition between adjacent hours. This procedure is based on the given standards dj and Fmj for each hour j and on the jth hourly max load, Lj. Procedure 2 has departure times that, on average, enable buses to carry even dj, Fmj, and individual bus loads at the hourly max load point where Lj is observed. Procedure 3 derives the departure times so that, on average, the onboard passenger load will not exceed dj and will be equal to dj at each individual bus max load point.

Ceder (2001) has addressed the problem of designing efficient bus

synchronization into a timetable. This study focuses on the maximum synchronization, which is important from the operator’s and user’s point of view. This involves creating timetables that will maximize the number of simultaneous arrivals of buses at the transfer nodes. Any route design at the network level attempts to eliminate a large number of transfer points, because of their adverse effect on the user. A trade-off exists between eliminating many transfer points and the efficiency of the bus route network from the operating cost perspective. To allow for an adequate bus level of service, schedulers, in facing the synchronization task, want to ensure maximum switching in the transfer of passengers from one route to another without wait time.

Procedures are presented to enable transit schedulers to set restrictions on

headways for each route, introduce different frequencies for every route, and apply other constraints. The objective is to maximize the number of simultaneous bus arrivals in the network. The departure times are set accordingly. 4.1.6 GIS and EMME/2 as decision support system

A synchronized and well-organized bus transport service includes large number of parameters. Manually, it may not be easy to handle all of those; geographical information system is a tool to represent a large amount of data effectively.

Jane et al. (1997) have developed a geographic information system that

includes the street maps for the three-country service region, the route system, and the bus stop locations. These maps are used together with US census block and block group information to perform communication, analysis, planning and service assurance.

Belinda M. Wu and Julian P. Hine (2003) conclude that GIS enables an

improved analysis of transport disadvantage and accessibility. Their paper provides an analysis of the city bus network in Northern Ireland and assesses the spatial impact of hypothetical network changes on populations residing within the city bus network area. Within the GIS environment, data sets are displayed in a


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range of innovative ways (3-D, grid and other thematic maps) to facilitate data interpretation.

Zhong-Ren Peng and Ruihong Huang (2000) present web-based transit

information system design that uses Internet Geographic Information Systems (GIS) technologies to integrate Web serving, GIS processing, network analysis and database management. A path finding algorithm for transit network is proposed to handle the special characteristics of transit networks, e.g., time-dependent services, common bus lines on the same street, and non-symmetric routing with respect to an origin/destination pair. The algorithm takes into account the overall level of services and service schedule on a route to determine the shortest path and transfer points. A framework is created to categorize the development of transit information systems on the basis of content and functionality, from simple static schedule display to more sophisticated real time transit information systems. A unique feature of the reported Web-based transit information system is the Internet-GIS based system with an interactive map interface. This enables the user to interact with information on transit routes, schedules, and trip itinerary planning. Some map rendering, querying, and network analysis functions are also provided.

Marius Theriault et al. (1999) presents a modeling and simulation

procedure to evaluate optimal routes and to compute travel times for each individual trip of an OD survey database. Postal codes provide accurate locations within street blocks for each trip beginning and end point. Using TransCAD GIS software, the procedure finds the best routes through a topological road network. Each road is characterized by a maximal speed related to the functional class of the road, to its location in rural or urban areas, and to the distance from the nearest school. Turn and transfer penalties govern movements at the intersections. Moreover, the procedure calculates the number of persons traveling on every road to estimate traffic congestion. It is concluded that it is possible to combine GIS and transportation modeling to estimate travel time of urban commuters. This could help in measuring temporal constraints of households in planning their daily activities.

Ali M. (2003) has focused on the two related issues of employment

distribution and access to transit services. It is primarily concerned with identifying some of the major employment centers in the county and how they may be understood within the context of a polycentric metropolitan area. Using the 2001 census tract level economic activities and the 2000 census of population and housing, a number of analyses were performed that identified some of the major employment centers and sub-centers in the metropolitan areas. A GIS-friendly software is used that allows calculations of various clustering patterns for major employers, total employment in 2001. This software performs an NNH (nearest neighbor hierarchical) spatial clustering routine that groups data points


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together on the basis of spatial proximity (using a threshold distance and the minimum number of points required for each cluster).

Transit accessibility is relatively high for a majority of sub-center clusters;

the geography of job-housing balance suggests a high level of spatial mismatch in the region, especially for the low-income and African-American populations. With the exception of a few locations, transit-rich areas are geographic spikes that converge on the downtown area. This creates a number of problems. For example, while employment subcenters are adequately networked by the existing bus routes, the connection between employees and their place of work appears to be inadequate. This is especially true for connections that require a north–south movement. In fact, the geography of transit network in the county shows a preponderance of east-west connections. This has created a sub optimal condition in many sections of the metropolitan area. The author concludes that the transit network should be conceptualized not only as the backbone of economy, but also as an urban service that enhances the economic vitality of all sectors of the society – especially those with the most need for mobility and improvement in their level of access to jobs and other urban amenities.

Michael Florian et al. (2003) note that in many cities of the developed and

developing world, certain transit services are overcrowded. One should also consider a fact that there is a need to model the congestion aboard the vehicles and the increased waiting time since passengers may not board the first vehicle to arrive at a stop.

Major existing transit route choice models do not consider such capacity

effects. It is not sufficient to impose a capacity constraint; one must be able to model the increased waiting time. As the transit segments become congested, the comfort level decreases and the waiting times increase. These phenomena are modeled with increasing convex cost functions to model discomfort and with increased headway to model increased waiting times. This criterion can be implemented by using EMME/2 macro language.

Bernard and Vladimir (1998) state that short and mid term planning tasks,

performed on an everyday basis in large public transit agencies, require a quick assessment of the impacts of service changes on their customers.

TTS (transportation tomorrow survey) has been done in 1986 for GTA

(Greater Toronto Area), which collected detailed demographic and travel information including trip data and location data, from which a database for EMME/2 was prepared. This was used for new trip assignments. Maximum distance of each route and number of transfer points were also implemented. Bernard and Vladimir (1998) shows (figure 4), the main steps involved in assigning individual transit trips using EMME/2.


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Figure 4: Main steps involved in assigning transit trips

( Source: Bernard and Vladimir 1998)

It provides a set of parameters i.e. number of transfer nodes, access distance, access/egress speed, wait time weight, boarding time weight, etc. These parameters provide a firm base from which to make informed and responsible decision on planning transit service for customers. Heinz Spiess (1993) indicates that in most transit assignment applications, congestion affects due to overcrowding of the vehicles are not taken into account for modeling of the route choice. This is a reasonable approach in all those cases where the goal of the planning process is to provide enough capacity for all transit passengers on the routes of their choice. There are, however, situations in which it is not feasible to provide enough transit capacity to preclude congestion. In these cases, the route choice of the transit passenger is likely to be influenced by the congestion on board the vehicles, so that some travelers will switch from congested to less congested routes, even if the latter are less attractive in terms of travel time or cost. In this paper, he describes implementation of an equilibrium transit assignment based on the concept of optimal strategies. Congestion is modeled by means of volume dependent cost functions, similar to the volume-delay functions used in the highway equilibrium assignment. Finally, mathematical formulation of the model is implemented in EMME/2.


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4.2 Infrastructure Modifications strategies

Several cities have enhanced bus system capacity by changing the infrastructure design. Bus systems, which have included operational and infrastructure modifications, are able to provide capacities ranging from 10,000 persons per hour in each direction to 35,000 persons per hour as shown in table 5.

Table 5: Ridership of BRT in different cities of world.

City/Country Ridership (PHPD)

Ottawa Transitway 10000 Goiania,Brazil 11500

Quito 15000 Curitiba Eixo Sul 15100

Belo Horizonte, Brazil 21100 Porto Alegre Farrapos 25600 Porto Algegre Assis 28000

Recife Caxanga 29800 Bogota TransMilenio 33000

Sao Paulo 34911 (Source: GTZ, 2002) High Capacity Bus System (HCBS) has become the most efficient,

affordable and above all sustainable mass transport system. Because of its flexibility, ease of implementation and image of a modern information technology based system, several Latin American and Chinese cities are adopting this in favor capital intensive Metro systems. Since mid seventies when the first system appeared in Curitiba, Brazil, the system design has been evolving as per the local needs- needs of road users, institutional mechanisms and financial structures.

HCBS implies creating a system which gives priority to bus commuters.

Since every bus commuter is also a pedestrian, the system must address the needs of pedestrians also. The system efficiency is achieved by creating exclusive lanes for buses. This minimizes conflicts with other vehicles and therefore improves bus movements Exclusive bus lanes can be provided either as curb-side lanes (Figure5 and Figure 6) or central lanes (Figure 7 and Figure 8) physically segregated from the rest of the traffic. 4.2.1 Curb-Side Bus Lanes

The bus lane is provided along the curb-side with the MV lane at the center of the corridor. The NMV lane and the pedestrian path are at the left of the bus lane. Locating the bus lane at the curb-side allows use of the existing


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infrastructure but a large volume of left turning MV traffic will interfere with buses running on the side lanes. The curb-side bus lane requires overtaking and breakdown lanes for buses on both sides of the corridor as opposed to the one common lane in the case of central bus lanes.

Figure 5: Curb-Side Lane

(Source: TRIPP, 2003)

Figure 6: Intersection design with curb-side bus lanes



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The central lane of a corridor is one with minimum conflict. There is least probability that the volume of turning traffic from innermost lane will come in conflict with it. These central lanes can be reserved for MVs or buses. In centrally placed bus lane layout the bus lanes for both directions are clubbed together so that common facilities like overtaking and breakdown lane can be shared as shown in Figure 8. The selection of the location of the bus lane is site specific as discussed in table 6.

Figure 7: Central Bus Lanes


Figure 8: Intersection design in case of central bus lane layout.



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Table 6: Criteria for site specific choice between a central bus-lane layout

and a curb-side bus lane layout

S.No. Central Bus Lane Curb-Side Bus Lane 1. Excessive side-entries for vehicles

into service lanes or individual plots. Limited access to service lanes or widely spaced entry points into adjoining area.

Rationale The high volume of turning traffic interferes with the through movement of bus traffic if the bus uses the same curb-side lane as the turning vehicles.

2. Closely placed traffic lights for vehicles may be combined with bus shelters.

Traffic lights at larger intervals.

Rationale Buses using the curb-side lane are forced to stop at every red signal with other vehicles reducing throughput, therefore central bus lanes are preferred.

3. Higher volume of two-wheeler and three-wheeler vehicles

Lower volume of two-wheeler and three-wheeler vehicles

Rationale High volumes of two-wheeler and three-wheeler vehicles interfere with the movement of buses in the curb-side lane especially at the bus-shelters where buses often cannot approach the designated bus-bays due to the three-wheelers parked there and the two-wheelers trying to overtake from the left-side. Also, the difference in sizes of these vehicles sharing the curb-side lane makes the situation unsafe for the smaller vehicles.

Eg. Arterials through heavy commercial landuse areas like Vikas Marg

Highways through large institutional areas like stretch of Ring Road in ITO area.

(Source: TRIPP,2003) 4.2.2 Bus Stop Location

The bus stops should be located where it is safe and convenient for bus commuters to reach. Bus stop locations must minimize the delays faced at junctions. Bus stop platform design should minimize boarding and alighting time.

Along the existing corridor most bus shelters are located near junctions. They are also the most favored place for locating bus shelters along the proposed central bus lane because of ease of access and facilitating easy change of routes. A minimum distance of 500 m is recommended between the bus shelters. Bus shelters at junction can be located before the junction or after it. The decision of locating a bus shelter (before or after) is carefully analyzed by simulating the traffic on the proposed corridor (see Figure 9). During simulation it is observed that locating the shelter before the junctions shows better throughput results. This is because when buses arrive at a junction the probability of getting a red light is always higher than green therefore when bus shelters are placed before the traffic signals the red phase is utilized for boarding and alighting thus minimizing


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delays. Otherwise buses have to stop twice, once at red light and again at the bus shelter after the junction. Bus shelters can be located before the junction itself or at some distance away from it. It has been observed that as a bus shelter is brought nearer to a junction the flow of traffic improves. However it also increases interference with turning traffic. It has been found in simulation experiment that bus shelters located at 20m before the junction give the best results.

Figure 9: Bus shelter location, before and after the junction.


4.2.3 Bus Shelter Layout (Linear Vs Parallel)

Therefore holding space is provided before the junction in the case of bus shelters placed before the junction. Bus throughput can be improved by providing overtaking opportunities at the bus shelter. This requires an extra lane at the bus shelter.

Bus shelter with requisite docking capacity can be designed with docking bays arranged in a continuous linear fashion along one bus lane, as a single bus shelter as a Linear Bay Bus Shelter (Figure 10). A gap of 18-20m is required between two parked buses to allow entry and exit of buses. The other bus lane can be used as an overtaking lane. Bus docking bays can also be distributed and arranged in parallel bus shelter along the two bus lanes as a Parallel Bay Bus Shelter (Figure 11). Both lanes have bus shelter and both buses are used for parked buses. This does not have an extra overtaking lane, however need for overtaking is reduced because buses can be assigned specific bus shelter


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depending on the route numbers thus reducing the number of buses waiting at the junction. For example straight and left turning buses can be in the left lane and straight and right turning buses can be in left lanes. At green signals both lanes can move forward. The two bus lanes are extended till 60m after crossing the junction. It ensures same holding capacity of buses after crossing the junction as it is at the bus shelter before the junction. It makes smooth entry of buses into the lanes.

Figure 10: Linear Bus Shelter


Figure 11: Parallel Bus Shelter



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Linear and parallel bus shelters are compared in detail in Table 7.

Table 7: Comparison between Linear and Parallel Bus Shelter

Linear Parallel Distance from Intersection

30m for first bus shelter

18m for first bus shelter

Total Throughput at intersection

Average 12 buses per cycle

Average 12 buses per cycle

Total width of bus lanes at bus shelter

10.5m, - 3.5m wide bus shelter, 3.5m wide bus parking lane and 3.5m wide overtaking lane

10.8m, including – 2 bus shelters 2.5m wide each and 2 bus lanes 2.9m wide each

Dwell time of buses 30 (boarding) + 5 (maneuvering) + 20 (travel to crossing) sec.

30 (boarding) + 10 (travel to crossing) sec

Discipline Manual enforced Design Enforced Option of overtaking and express buses

Exists Restricted

Options in case of break down

Overtaking lane is used-but delays caused if bus not removed quickly

2nd bay takes the load of all routes – but delays caused if bus not removed quickly

Option of turnings at intersection

Exists with lane changing after last bay in 30m zone

Exists with each bay dedicated to either left of right turning buses.

Commuter walk time from intersection to the front of last bus at 1m/sec (end of the bus platform)

120 sec 60 sec

(Source: TRIPP,2003)

HCBS can have additional efficiency by changing the bus designs which

can carry more people, boarding and alighting is made efficient by wider doors and low floor-same height as bus stop platforms. Many cities have reorganized the institutional mechanisms and evolved public private partnerships for better regulations, licensing procedures and improved services. HCBS efficiency has


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further been improved by making use of information technology for better passenger information systems, route tracking, vehicle maintenance and safety systems. These different stages of HCBS can be introduced in phases, with each stage adding to the benefits of earlier system. 4.2.4 System Options

When designing an HCBS, there are two distinct but related decisions that need to be made. First, one has to decide whether to design a ‘closed’ busway with pre-paid enclosed bus stations at each stop, like in Curitiba, Quito, and Bogota, or an ‘open’ system, like in Taipei, Sao Paulo, Kunming, or Porto Allegre, where buses operate both inside the busway and also off the busway. Most HCBS are either one or the other. On ‘closed’ systems, like in Bogota and Curitiba, passengers pay when they enter an enclosed bus stop. Once inside the bus stop, passengers can board and alight from the buses very fast, and can transfer to other buses that stop at the same bus stop without paying again. The HCBS systems with the highest capacity and operating speeds are all ‘closed’ systems, because this pre-paid boarding dramatically reduces the amount of time it takes for passengers to board. Closed systems, because they load from a boarding platform, require the use of special buses. Because the systems are physically ‘closed,’ it is easy to change the contracting and regulatory structures inside that system without changing everything about how buses outside the system are regulated and managed. Closed systems tend to have a clear marketing identity, using special buses and a simple metro- like map of routes that constitute part of a fully separate system, usually served by special feeder buses.

The alternative is to design an ‘open’ system. In an open system, bus lanes

are constructed in the road as in a ‘closed’ system, but all buses using the corridor generally must enter or exit the busway. Because such systems are open to normal buses that also operate on normal streets, they tend to lack a clear marketing identity, and often go unrecognized as a ‘system’ by the general public. Some ‘open’ systems also have platforms level with the bus floor to allow for rapid boarding and alighting as in closed systems, but payment is still generally on the bus. Boarding delay can be reduced by having a boarding platform, and a boarding bay and a turnstile inside the bus, and only allowing passengers to exit from either the front or the rear, but boarding will still be slower than in ‘closed’ systems. ‘Open’ systems with boarding platforms increase the busway capacity above that in normal open systems, but this requires buses that have multiple door-types. Because normal buses are not held to a high maintenance regime, bus breakdowns inside ‘open’ busways are more frequently a problem.

The possibility exists to have a partially closed system, where special

buses operate both on and off a closed busway. Special buses operated by the HCBS authority could operate both on and off a physically separated busway


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corridor by having multiple door types and having passengers pay the driver when off the trunk corridor and paying at the bus stations when on the trunk corridor. 5. CONCLUSION

Bus systems have evolved from single route operations in small to medium size cities to high capacity systems in large urban agglomerations. Improvement in capacity utilization and operating costs have come from incremental changes in bus stop locations, scheduling, route operations, route planning, fleet management, special infrastructure designs, and institutional structures. This shows possible capacity improvement because of incremental changes in various aspects. Bus transportation is a predominant mode of urban transit. As cities grow the bus system is required to cater to a spatially and temporally diverse demand. The present literature review shows that substantial improvement in services and performance of the system is possible by employing better methods for route optimization and synchronization of feeder services. However, with the growth in the geographical extent of urban areas, GIS based decision support systems have been found to be very effective in designing optimal bus services. Bus transport service should provide comfortable traveling, reasonable fare and minimum time for traveling. Providing efficient road network, optimal routing and minimum delay can ensure these attributes. The system efficiency can be guaranteed by total synchronization with all other related factors i.e. route optimization, transfer optimization, and feeder bus service co-ordination. REFERENCES

1. Ali Modarres (2003) “Polycentricity and transit service”, Transportation Research Part – A, volume 37, pp. 841 – 864.

2. Andrzej Adamski and Zofia Bryniarska (1996) “Schedule synchronization in public transport by tabu search and genetic method”, Division of Transport Organization, Dept. of Civil Engineering, Cracow University of Technology, Poland.

3. Belinda M. Wau and Julian P. Hine (2003) “A PTAL approach to measuring changes in bus service accessibility”, Transport Policy, volume 10, pp. 307 – 320.

4. Benjamin Zhan and Charles Noon (1998) “Shortest path algorithms: An evaluation using real road networks”, Transportation Science 1998, volume 32, No. 1

5. Bernard Farrol and Vladimir Livshits (1998) “Analysis of individual transit trips in EMME/2: Calibration of 1996 TTC trips Disaggregate Assignment” source: http://www.jpint.utoronto.ca/PDF/transit_emme.pdf


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6. Bernard and Vladimir (1998)“Route” (1992), Communication of the ACM, January 1992/volume 35, No 1

7. Ceder, A. et al. “Creating bus timetables with maximal synchronization”, Transportation research part A, 35 (2001) 913-928

8. Ceder, A. and Ofer Tal (2001) “Designing synchronization into bus timetables”, Transportation Research Record 1760, paper no. 01-0442

9. Ceder A. (2001) “Bus timetables with even passenger loads as opposed to even headways”, Transportation Research Record 1760, paper no. 01-0443

10. Registrar General, India (2001) ‘Census of India – 2001’ published by Government of India.

11. Heinz Spiess (1993) “Transit equilibrium assignment based on optimal strategies: An implementation in EMME/2”, http://www.spiess.ch/emme2/congtras/congtras.html

12. Jane L. Crowson et al. (1997) “A GIS for public transport”, ESRI conference, San Diego – CA.

13. Marius Theriault et al. (1999) “Modeling commuter trip length and duration within GIS: Application to an OD survey”, Journal of Geographical and Decision Analysis, volume 3, no. 1, pp. 40-56

14. Meirelles Alexandre (2000), “A review of Bus priority system in Brazil: from bus lanes to busway transit”, Smart /urban Transport Conference, 17-20 October 2000, Brisbance Australia.

15. Micheel Florian, Shuguang He and Isaballe Constantin (2003) “An EMME/2 macro for transit equilibrium assignment with capacity considerations”, 17th Annual Internationl EMME/2 user’s conference.

16. Advani Mukti and Tiwari Geetam (2004), “Evaluation of Public Transport systems: Case study of Delhi metro”, Proceedings of International conference on Structural and Road transport engineering, IIT-Kharagpur.

17. Paolo Delle Site and Francesco Filippi (1998) “Service optimization for bus corridors with short-turn strategies and variable vehicle size”, Transportation research A, vol. 32, No. 1, pp. 19-38

18. Kalaga R. R.and Vishal Haripersad (1999) “Transfer optimization of a urban bus transit network”, source: http://[email protected]/cupum/pdf.T8.pdf

19. Steven I-Jy Chien et al. (2003) “Optimization of multiple-route feeder bus service – an application of GIS”, TRB 2003 Annual Meeting CD-ROM, Paper no. 03-2752


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20. Vincent Cardone (2003) “Optimization of a feeder bus service to sandy hook”, TRB 2003 Annual Meeting CD-ROM, Paper no. 03-2754

21. Zhong-Ren Peng and Ruihong Huang (2000) “Design and development of interactive trip planning for web-based transit information systems”, Transportation Research Part C, vol-8, pg. 409-425.

22. Tiwari G. (Ed) (2002), “Urban Transport for Growing Cities’ published by Macmillan India.

23. GTZ (2002), “Mass Transit Options” Sustainable Transport: A sourcebook for developing countries published by Division 44, Postfach 5180, 65726 Eschborn, Germany.

24. Transportation Research and Injury Prevention Programme (2003)”Design specifications for HCBS corridor” report submitted to Transport Department , Delhi Government for High Capacity Bus System, Indian Institute of Technology Delhi.

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