Estimating the demand for urban bus travel

The disadvantages of conventional transportation study models, in particular their large data requirements and their weaknesses in dealing with changes in trip generation rates have led to a need for a simple model that can quickly and at low cost examine alternative public transport strategies.This paper investigates simple economic models of bus demand, examines alternative variables that can be used and discusses some alternative model forms. It demonstrates the results of a model using data from twelve urban bus operators in Britain and compares the results with those from other types of study. The model utilises fare and service quality elasticities to explain the decline in passengers on urban bus services, and derives an average elasticity with respect to fare changes of –0.31 and with respect to service quality changes of +0.62. It is estimated that fare rises accounted for 13% of the 43% decline in passengers over the last fifteen years, vehicle mileage reductions for 14.3% and that only 15.7% was due to such factors as rising car ownership which are often given as the cause of declining bus patronage.The results, by showing that passengers are far more sensitive to changes in service than they are to fare rises, are a useful guide to the broader public transport policy issues, and the paper concludes that the model does provide a useful method of forecasting public transport demand at a strategic level. Further work is needed, however, to establish more accurate forecasts for different types of passenger and studies are now being undertaken to establish these and to construct an operational forecasting model that can be applied with only limited data requirements     

Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model

Although smart-card data were expected to substitute for conventional travel surveys, the reality is that only a few automatic fare collection (AFC) systems can recognize an individual passenger's origin, transfer, and destination stops (or stations). The Seoul metropolitan area is equipped with a system wherein a passenger's entire trajectory can be tracked. Despite this great advantage, the use of smart-card data has a critical limitation wherein the purpose behind a trip is unknown. The present study proposed a rigorous methodology to impute the sequence of activities for each trip chain using a continuous hidden Markov model (CHMM), which belongs to the category of unsupervised machine-learning technologies. Coupled with the spatial and temporal information on trip chains from smart-card data, land-use characteristics were used to train a CHMM. Unlike supervised models that have been mobilized to impute the trip purpose to GPS data, A CHMM does not require an extra survey, such as the prompted-recall survey, in order to obtain labeled data for training. The estimated result of the proposed model yielded plausible activity patterns that are intuitively accountable and consistent with observed activity patterns.     

Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data

Transit fares are an effective tool for demand management. Transit agencies can raise revenue or relieve overcrowding via fare increases, but they are always confronted with the possibility of heavy ridership losses. Therefore, the outcome of fare changes should be evaluated before implementation. In this work, a methodology was formulated based on elasticity and exhaustive transit card data, and a network approach was proposed to assess the influence of distance-based fare increases on ridership and revenue. The approach was applied to a fare change plan for Beijing Metro. The price elasticities of demand for Beijing Metro at various fare levels and trip distances were tabulated from a stated preference survey. Trip data recorded by an automatic fare collection system was used alongside the topology of the Beijing Metro system to calculate the shortest path lengths between all station pairs, the origin–destination matrix, and trip lengths. Finally, three fare increase alternatives (high, medium, and low) were evaluated in terms of their impact on ridership and revenue. The results demonstrated that smart card data have great potential with regard to fare change evaluation. According to smart card data for a large transit network, the statistical frequency of trip lengths is more highly concentrated than that of the shortest path length. Moreover, the majority of the total trips have a length of around 15 km, and these are the most sensitive to fare increases. Specific attention should be paid to this characteristic when developing fare change plans to manage demand or raise revenue.     

Demand-driven timetable design for metro services

Timetable design is crucial to the metro service reliability. A straightforward and commonly adopted strategy in daily operation is a peak/off-peak-based schedule. However, such a strategy may fail to meet dynamic temporal passenger demand, resulting in long passenger waiting time at platforms and over-crowding in trains. Thanks to the emergence of smart card-based automated fare collection systems, we can now better quantify spatial–temporal demand on a microscopic level. In this paper, we formulate three optimization models to design demand-sensitive timetables by demonstrating train operation using equivalent time (interval). The first model aims at making the timetable more dynamic; the second model is an extension allowing for capacity constraints. The third model aims at designing a capacitated demand-sensitive peak/off-peak timetable. We assessed the performance of these three models and conducted sensitivity analyzes on different parameters on a metro line in Singapore, finding that dynamical timetable built with capacity constraints is most advantageous. Finally, we conclude our study and discuss the implications of the three models: the capacitated model provides a timetable which shows best performance under fixed capacity constraints, while the uncapacitated model may offer optimal temporal train configuration. Although we imposed capacity constraints when designing the optimal peak/off-peak timetable, its performance is not as good as models with dynamical headways. However, it shows advantages such as being easier to operate and more understandable to the passengers.     

Passengers’ response to transit fare change: an ex post appraisal using smart card data

Fare change is an effective tool for public transit demand management. An automatic fare collection system not only allows the implementation of complex fare policies, but also provides abundant data for impact analysis of fare change. This study proposes an assessment approach for analyzing the influence when substituting a flat-fare policy with a distance-based fare policy, using smart card data. The method can be used to analyze the impact of fare change on demand, riding distances, as well as price elasticity of demand at different time and distance intervals. Taking the fare change of Beijing Metro implemented in 2014 as a case study, we analyze the change of network demand at various levels, riding distances, and demand elasticity of different distances on weekdays and weekends, using the method established and the smart card data a week before and after the fare change. The policy implication of the fare change was also addressed. The results suggest that the fare change had a significant impact on overall demand, but not so much on riding distances. The greatest sensitivity to fare change is shown by weekend passengers, followed by passengers in the evening weekday peak time, while the morning weekday peak time passengers show little sensitivity. A great variety of passengers’ responses to fare change exists at station level because stations serve different types of land usage or generate trips with distinct purposes at different times. Rising fares can greatly increase revenue, and can shift trips to cycling and walking to a certain extent, but not so much as to mitigate overcrowding at morning peak times. The results are compared with those of the ex ante evaluation that used a stated preference survey, and the comparison illustrates that the price elasticity of demand extracted from the stated preference survey significantly exaggerates passengers’ responses to fare increase.     

Nonlinear pricing of taxi services

This paper examines the effects of nonlinear fare structures in taxi markets using an extended taxi model with an explicit consideration of perceived profitability. The expected profit, defined as the profit per unit time (inclusive of both occupied and vacant taxi times), that a taxi driver expects to receive from picking up a customer in a particular zone or location, has great impact on the taxi driver’s choice of location in the search for customers. The fare structure directly governs the profitability of taxi rides of different distances originating from different locations. With these explicit considerations, the extended model is intended to look into the market effects of adopting a nonlinear fare structure with declining incremental charges. The proposed nonlinear fare structure could help restore a level-playing field for taxi operators whose businesses have been affected by some taxi drivers who resort to practices such as offering fare discounts or accepting requests for discounted fares from passengers for long-haul trips. Analysis of sensitivity of social welfare and profit gain as well as taxi/customer wait/search times is conducted with respect to the parameters in the nonlinear fare structure for the Hong Kong taxi market, and Pareto-improving nonlinear fare amendments are identified that neither disadvantage any customer nor reduce the taxi operators’ profits.     

A theoretical and empirical model of trip chaining behavior

This paper addresses the theoretical and empirical issues involved in modeling complex travel patterns. Existing models have the shortcoming of not representing the interdependencies among trip links in trip chains with multiple non-home stops. A theoretical model based on utility theory and explicitly accounting for the trade-offs involved in the choice of multiple-stop chains is developed. Using this theoretical model, utility maximizing conditions for a household's choice of a daily travel pattern are derived. The optimum travel pattern is described in terms of the number of chairs (tours) traveled on a given day and in terms of the number of stops (sojourns) made on each of those chains. For a given household, the form of the optimum pattern is a function of the transportation expenditures (time, cost) required to reach potential destinations. Constraints on the conditions of optimality due to the limited and discrete nature of travel pattern alternatives are also considered. Parameters of the general utility function were estimated empirically using actual travel data derived from a home interview survey taken in Washington, D.C. The multinomial logit model is used to relate utility scores for the alternative travel patterns to choice probabilities. The resulting parameter estimates agree with theoretical expectations and with empirical results obtained in other studies. In order to demonstrate the empirical and theoretical implications of the model, forecasts for various transportation policies (e.g., gasoline price increases, transit fare reductions), as made by this model and by other less complex models, are compared. The results of these comparisons indicate the need for expanding the scope of existing travel forecasting models to explicit considerations of trip chaining behavior.     

Imperfect reversibility of air transport demand: Effects of air fare,fuel prices and price transmission

There are recent evidence that air transport demand may not have a perfectly reversible relationship with income and jet fuel prices, as is assumed in most demand models. However, it is not known if the imperfectly reversible effects of jet fuel price are a result of asymmetries in the supply side, i.e., asymmetries in cost pass through from fuel prices to air fare, or of demand side behavioral asymmetries whereby people value gains and losses differently. This paper uses US time series data and decomposes air fare and fuel price into three component series to develop an econometric model of air transport demand that is capable of capturing the potential imperfectly reversible relationships and test for the presence or absence of reversibility. We find that air transport demand shows asymmetry with respect to air fare, indicating potential imperfect reversibility in consumer behavior. We also find evidence of asymmetry and hysteresis in cost pass-through from jet fuel prices to air fare, showing rapid increases in airfare when fuel prices increases but a slower response in the opposite direction.     

Mixed RP/SP models incorporating interaction effects

In order to analyse the impact of a new train service in Cagliari (Italy) a databank including information from a revealed preference (RP) and a stated preference (SP) survey was set up. The RP data concern choice between car, bus and train; the SP data consider the binary choice between a new train service (quicker, more frequent, with a lower fare and more stations than the current one) and the alternative currently chosen by car and bus users. Logit models allowing for correlation among RP alternatives were estimated for this mixed RP/SP data set using the artificial tree structure method. The analysis included level-of-service variables measured with an unusually high level of precision, latent or second order variables (such as comfort), inertia and interaction variables. Different specifications of the utility function were tested, including the expenditure rate model, and the effects of these specifications on modelling results are highlighted. Our results show that for a population mainly composed of fixed income workers, the expenditure rate model is superior to the traditional wage rate model, yielding lower and more significant subjective values of time. Moreover, we found that the non-linear specifications appear to be more suitable as not only better model results were obtained, but also the real distribution of the error terms was revealed (i.e. highlighting correlation among public transport options).     

Analyzing year-to-year changes in public transport passenger behaviour using smart card data

In recent years, there has been increased interest in using completely anonymized data from smart card collection systems to better understand the behavioural habits of public transport passengers. Such an understanding can benefit urban transport planners as well as urban modelling by providing simulation models with realistic mobility patterns of transit networks. In particular, the study of temporal activities has elicited substantial interest. In this regard, a number of methods have been developed in the literature for this type of analysis, most using clustering approaches. This paper presents a two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage. The strength of the proposed methodology is that it can model a continuous representation of time instead of having to employ discrete time bins. For each cluster, the approach provides typical temporal patterns that enable easy interpretation. The experiments are performed on five years of data collected by the Société de transport de l’Outaouais. The results demonstrate the efficiency of the proposed approach in identifying a reduced set of passenger clusters linked to their fare types. A five-year longitudinal analysis also shows the relative stability of public transport usage.     

Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations

Transportation - Smart card data derived from automatic fare collection (AFC) systems of public transit enable us to study resident movement from a macro perspective. The rhythms of traffic...     

A comparative study of aggregate and disaggregate gravity models using Seoul metropolitan subway trip data

Abstract

Existing origin constrained and doubly constrained gravity models have not been compared, theoretically or empirically, in terms of their forecasting power. Due to the newly advanced technology of intelligent transport systems, the expanded data presently available have made various models more comparable in terms of forecasting power. This paper uses archived automatic passenger counting (APC) data for urban rail in the Seoul metropolitan area. The APC data contains information about each trip's origin, destination, ticket type, fare, and distance on a daily basis. The objective of this paper is to compare the goodness-of-fit of aggregate and disaggregate gravity modeling using these data. A Hyman aggregate gravity model is used as the aggregate model without the spatial effect. The disaggregate model adopts a multinomial logit as the destination choice model with the spatial effect. In general, while the formulation of aggregate and disaggregate gravity model models are similar, the calibration and parameter estimation methods of the two models are different. As a result, this empirical study demonstrates that the variation in goodness-of-fit and forecasting power largely depends on the estimation method and selected variables. The forecasting power of the disaggregate modeling approach outperforms that of the aggregate model. This paper further confirms that spatial arrangement plays important roles in gravity modeling.     

Estimating the influence of common disruptions on urban rail transit networks

With the continuous expansion of urban rapid transit networks, disruptive incidents—such as station closures, train delays, and mechanical problems—have become more common, causing such problems as threats to passenger safety, delays in service, and so on. More importantly, these disruptions often have ripple effects that spread to other stations and lines. In order to provide better management and plan for emergencies, it has become important to identify such disruptions and evaluate their influence on travel times and delays. This paper proposes a novel approach to achieve these goals. It employs the tap-in and tap-out data on the distribution of passengers from smart cards collected by automated fare collection (AFC) facilities as well as past disruptions within networks. Three characteristic types of abnormal passenger flow are divided and analyzed, comprising (1) “missed” passengers who have left the system, (2) passengers who took detours, and (3) passengers who were delayed but continued their journeys. In addition, the suggested computing method, serving to estimate total delay times, was used to manage these disruptions. Finally, a real-world case study based on the Beijing metro network with the real tap-in and tap-out passenger data is presented.     

Assessing social equity in distance based transit fares using a model of travel behavior

The goal of this study is to develop and apply a new method for assessing social equity impacts of distance-based public transit fares. Shifting to a distance-based fare structure can disproportionately favor or penalize different subgroups of a population based on variations in settlement patterns, travel needs, and most importantly, transit use. According to federal law, such disparities must be evaluated by the transit agency, but the area-based techniques identified by the Federal Transit Authority for assessing discrimination fail to account for disparities in distances travelled by transit users. This means that transit agencies currently lack guidelines for assessing the social equity impacts of replacing flat fare with distance-based fare structures. Our solution is to incorporate a joint ordinal/continuous model of trip generation and distance travelled into a GIS Decision Support System. The system enables a transit planner to visualize and compare distance travelled and transit-cost maps for different population profiles and fare structures. We apply the method to a case study in the Wasatch Front, Utah, where the Utah Transit Authority is exploring a switch to a distance-based fare structure. The analysis reveals that overall distance-based fares benefit low-income, elderly, and non-white populations. However, the effect is geographically uneven, and may be negative for members of these groups living on the urban fringe.     

Cost and time damping: evidence from aggregate rail direct demand models

There is a significant body of evidence from both disaggregate choice modelling literature and practical travel demand forecasting that the responsiveness to cost and possibly to time diminishes with journey length. This has, in Britain at least, been termed ‘Cost Damping’, and is recognised in guidance issued by the UK Department for Transport. However, the consistency of the effect across modes and data types has not been established. Cost damping, if it exists, affects both the forecasting of demand and our understanding of behaviour. This paper aims to investigate the evidence for cost and time damping in rail demand using aggregate rail ticket sales data. The rail ticket sales data in Britain has, for many years, formed the basis of analysis of a wide range of impacts of rail demand. It records the number of tickets sold between station pairs, and it is generally felt to provide a reasonably accurate reflection of travel demand. However, the consistency of these direct demand models with choice modelling and highway demand model structures has not been investigated. Rail direct demand models estimated by ticket sales data indicate only slight variation in the fare elasticity with distance, as is evidenced in the largest meta-analysis of price elasticities conducted to date (Wardman in J Transp Econ Policy 48(3):367–384, 2014). This study of UK elasticities shows strong variation between urban and inter-urban trips, presumably a segmentation at least in part by purpose, but less remaining variation by trip length. A lack of variation by length supports the hypothesis of cost damping, because constant cost sensitivity would imply that fare elasticity would increase strongly with distance, because of the increasing impact of higher fares at longer distances. In this paper we indicate that rail direct demand models have some consistency of behavioural paradigm with utility based choice models used in highway planning. We go on to use rail demand data to estimate time and fare elasticities in the context of various cost damping functions. Our empirical contribution is to estimate time elasticities on a basis directly comparable with cost elasticities and to show that the phenomenon of cost damping is strongly present in ticket sales data. This finding implies that cost damping should be included in models intended for multimodal analysis, which may otherwise give incorrect predictions.     

The main determinants of the demand for public transport: a comparative analysis of England and France using shrinkage estimators

This study analyses the impacts of changes in fares, service supply, income and other factors on the demand for public transport on the basis of panels of English counties and French urban areas. The analysis is based on dynamic econometric models, so that both short- and long-run elasticities are estimated. Conventional approaches (i.e. fixed- and random-effect models) rely on the hypothesis that elasticities are the same for all areas. Having shown that this hypothesis is not valid for these data sets, the heterogeneity amongst areas is accounted for using a random-coefficients approach, and Bayesian shrinkage estimators.Estimated elasticities for France and England are compared, by using a common set of variables, similar time period and a common methodology. The results show a considerable variation in elasticities among areas within each country. The major conclusion is that public transport demand is relatively sensitive to fare changes, so that policy measures aimed at fare reduction (subsidisation) can play a substantial role in encouraging the use of public transport, thus reducing the use of private cars.     

Normative framework for transit fare policy-making

This paper presents a normative model for transit fare policy-making. Key elements of the model are: establishing service policy and ridership objectives, developing an overall financial philosophy, making fare level decisions, making structural pricing decisions, and designing implementation strategies. In general, the overall objectives of a transit agency regarding service quality and ridership levels should be the main impetus behind any fare program. Identifying where transit lies on the continuum of being a public versus a private service should frame the overall financial philosophy of a transit agency. From this the specification of farebox recovery targets should follow. Deciding upon structural aspects of a fare program perhaps represents one of the most important and most frequently overlooked steps of the process. Specific cost-based and value-based fare strategies should be considered. Implementation involves making the adopted fare strategy work. Key implementation issues are: fare payment and collection techniques, necessary service changes, marketing and promotional programs, and consensus-building. The model presented calls for feedback among these steps to allow an iterative, yet comprehensive, approach to fare policy-setting.     

Coordination of last train transfers using automated fare collection (AFC) system data

This paper studies last train coordination problem for metro networks, aiming to maximize the total number of passengers who can reach their destinations by metro prior to the end of operation. The concept of last boarding time is defined as the latest time that passengers can board the last trains and reach final destinations. The corresponding method for calculating last boarding time is also put forward. With automatic fare collection system data, an optimization model for coordinating last trains is proposed. The objective function optimizes the number of passengers who can reach their final destinations during the train period using departure times and headways of last trains for each line as decision variables. Afterwards, an adaptive genetic algorithm is put forward to solve this model and is applied to a case study of the Shanghai metro system. Copyright © 2017 John Wiley & Sons, Ltd.     

A quadratic programming model for designing and evaluating distance-based and zone fares for urban transit

A microcomputer based system for designing and evaluating distance-based and zone fares for transit properties is described. At the heart of the system is an optimization model that finds the fixed charge, mileage charge, and transfer charge that maximize gross revenue subject to constraints on ridership and the form of the fare equation. A linear approximation to the demand curve at the base case values results in a quadratic programming problem. Three alternative modes of using the model system are demonstrated using selected data from the Chicago Transit Authority. Model extensions and proposed future work are outlined.     

Automatic ticketing on London underground

London Underground is currently going through a revolution in its fare collection systems. Magnetically encoded tickets are being introduced, obtainable from ticket office or self‐service machines, which are then checked automatically at start or finish of journey in the Underground's central zone. The microprocessor controlled equipment handles all accounting aspects of fare collection, because it is all part of a computer‐based network with centralized monitoring and control. The machinery was progressively installed from April 1987 to December 1988. This paper describes the history of the system, describes the new ticket and issuing machines, as well as discussing the development and project management tasks involved.