DESIGN OF AN URBAN PUBLIC ROAD TRANSPORT SYSTEM

Abstract

Transport plays an extremely important role in the quality of life of urban dwellers. Bus transport is the principal mode of transport for most of the urban population in India due to its various advantages. In India almost all State Road Transport Undertaking (STUs) are under loss. The government is reluctant to extend financial assistance to bus operators. It is therefore, imperative that bus transport systems should be designed effectively and efficiently. However, at the same time, bus transport systems have been labelled as a social service, as a means of avoiding congestion and a vehicle for reducing pollution. It is categorised, as the mode of transport that is available to everybody especially to those of economically weaker sections. The objectives of the bus operator are minimisation of the operating cost, maximisation of the utilisation of resources or productivity, and profit maximisation. Operators are keen to know at what operating frequency of buses on a route or fleet strength or bus utilisation, their objectives are fulfilled. On the other hand, users are interested in minimisation of cost of travel which includes fares and the value of time of journeys constituted by waiting time, in-vehiclc time, and walking time. If all these components are labelled as - generalised cost, then users are interested in the minimisation of their generalised cost. Generally the objectives of operators and users are in conflict, so a new measurement-that is net social benefit-is taken which considers the interest of both operator and user simultaneously for the design of the urban bus transit system. While designing the urban bus transit system on the basis of the operator's interest, one should know the operating cost of bus operation and demand for the bus. Proper identification of the relationship between the demand for travel and the variables that influence travel demand enable the transportation planner to determine the future demand for travel and design of the fleet size. Moreover, the cost models developed can be utilised to assess the transport system whether the resources (buses) are optimally utilised or under-utilised. For the development of the cost model, data from Trivandrum city depot of Kerala State Road Transport Corporation (KSRTC) for 16 routes having different bus utilisation rates were collected. Fixed cost and operating cost have been calculated from the Administrative Report published by the KSRTC. To ascertain the maximisation of the utilisation of bus in km or minimisation of the cost of operation in Rs/km, marginal cost concept is used. The variation of the average total cost (sum of operating cost and fixed cost), average variable cost and marginal cost over a range of bus-km operated have been determined. Plots of these costs against the bus-km are necessary to locale the intersection of the average total cost with marginal cost. The corresponding value on the x-axis gives the maximisation of bus utilisation or productivity and the value on the y-axis provides the total minimum cost per kilometer operation of bus. Similarly the intersection of the average operating cost with marginal cost c provides bus-km utilisation at the minimum operating cost. The economic evaluation through the marginal cost concept revealed that the system is under-utilised. From the study, it was found that total cost of bus-km operation at 382 km provides a minimum total cost of 5.15 Rs/km. For the variable cost, optimum values are 258.50 km and 2.57 Rs/km respectively. VI Urban bus travel demand analysis aims at relating bus travel to the socio economic and demographic characteristics of travellers and to the level of service (supply) characteristics of the transportation system. Various variables like population, workers, distance from CBD, distance between origin and destination, and connectivity for each zone were considered under socio-economic and demographic characteristics. Zones were established on the basis of fare stage followed by the operator. Under the level of service characteristics, generalised cost is considered and are determined by summing user's out-of-pocket cost and the monetary value of in-vehicle time, and out-of vehicle time. To ascertain the value of time. Binary Logit Model was used. A questionnaire was prepared to acquire data for logit model calibration. The samples were drawn from various strata of commuters based on sex, income groups, mode used, trip length and time slots. The required data were collected from the study area having 4 routes, where optimisation was envisaged. This study area has 11 zones of heterogeneous land use. 250 records were collected by random sampling method. From the survey, it is observed that users have different perceptions towards the different time slots that is peak and off-peak travel. Value of time for in-vehicle and out-of-vehicle time for peaks and off-peak was estimated. Bus passenger Origin Destination (O-D) c matrix was generated from on-off data retrieved from the trip sheet. From the past studies, it is seen that there is temporal variation in demand. Therefore, in this study 3 separate models for morning peak, off-peak and evening peak were developed. Stepwise regression method was used for the determination of coefficients of the variables. Many functional forms between socio-economic variables were tried and the product form proved to be more acceptable because of the higher t-values, F-value and coefficient of determination. The significant variables to explain the model are population, workers, generalised cost, distance Vll from CBD and a dummy variable which signifies whether the trip is terminated or originated from depot. It was considered because almost all bus trips are terminated or originated from the depot. The model was used to predict the trips on a route which was not under the study area, and was found to be transferable. The routes in the study area show properties of overlapping at certain distances, before diverging. So an analytical approach to optimising, the maximum profit become very complex after incorporating the capacity of the bus as one of the constraints. A heuristic approach to optimise bus operation has been used in ^ this work. The component analysis strategy has been adopted in the heuristic procedure. Apart from the demand model, a cost model for the consumption of fuel was also developed and incorporated. The significant variables affecting the fuel consumption are length of the routes, number of bus stops served and total stopped-time including dead time to serve the passengers at bus stop. The stopped- time utilised in serving passengers at bus stops varies with time slot. Though the number of passengers to be served at a bus stop during peak and off-peak, is the same, the marginal rate of boarding and alighting of passenger and the dead time are different. Applying this model to the system helps to show that different levels of fuel consumption occur during different time slots. In other words, in serving a particular route during peak and off-peak, a bus needs different quantity of fuel which makes cost of operation different for different time slots. From the demand and cost models, revenue and cost of operation for each additional bus on the four routes of the corridor can be used to find the optimum frequency for maximum profit. Optimum frequency for maximum profit for morning peak, offpeak and evening-peak have been deduced. Vlll For the maximisation of social welfare, change in consumer's surplus and producer's surplus - which is revenue minus operating cost - are added. The presence of the consumer's surplus considers the users benefit, and producer's surplus takes into account the interest of the operator. So net social benefit (NSB) can be treated as a balanced measured of the system operation. For each additional input of bus, the cost, revenue and the change in consumer's surplus have been worked out to determine the net social benefit. Bus frequency is increased in the model until there is a fall in the NSB. The frequency corresponding to the highest value of NSB is the optimum frequency for net social benefit. NSB for morning peak, off-peak and evening peak have been evaluated and corresponding optimum frequencies determined. The general view is that bus transport is typically not a business enterprise, but should be a public utility with a dominant social purpose. The change in net social benefit under various fare options are computed. Generalised cost (GC) is found to be the most significant supply variable in the urban bus transport demand models. A reduction on the GC attracted more passengers than corresponding reduction on any other independent variables on the demand model. The change in net social benefit under several subsidy options has also been analysed. Any increase in subsidy to the operators to improve the productivity is beneficial to the users.