Passenger Perspectives in Railway Timetabling: A Literature Review

When looking at railway planning, a discrepancy exists between planners who focus on the train operations and publish fixed railway schedules, and passengers who look not only at the schedules but also at the entirety of their trip, from access to waiting to on-board travel and egress. Looking into this discrepancy is essential, as assessing railway performances by merely measuring train punctuality would provide an unfair picture of the level of service experienced by passengers. Firstly, passengers’ delays are often significantly larger than the train delays responsible for the passengers to be late. Secondly, trains’ punctuality is often strictly related to too tight schedules that in turn might translate into knock-on delays for longer dwelling times at stations, trip delays for increased risk of missing transfer connections, and uncertain assessment of the level of service experienced, especially with fluctuating passenger demand. A key aspect is the robustness of railway timetables. Empirical evidence indicates that passengers give more importance to travel time certainty than travel time reductions, as passengers associate an inherent disutility with travel time uncertainty. This disutility may be broadly interpreted as an anxiety cost for the need for having contingency plans in case of disruptions, and may be looked at as the motivator for the need for delay-robust railway timetables. Interestingly, passenger-oriented optimisation studies considering robustness in railway planning typically limit their emphasis on passengers to the consideration of transfer maintenance. Clearly, passengers’ travel behaviour is far more complex and multi-faceted and thus several other aspects should be considered, as becoming more and more evident from passenger surveys. The current literature review starts by looking at the parameters that railway optimisation/planning studies are focused on and the key performance indicators that impact railway planning. The attention then turns to the parameters influencing passengers’ perceptions and travel experiences. Finally, the review proposes guidelines on how to reduce the gap between the operators’ railway planning and performance measurement on the one hand and the passengers’ perception of the railway performance on the other hand. Thereby, the conclusions create a foundation for a more passenger-oriented railway timetabling ensuring that passengers are provided with the best service possible with the resources available.     

The variation in the value of travel-time savings and the dilemma of high-speed rail in China

This paper examines the variation in the value of travel-time savings (VTTS), a fundamental element determining the market demand for high-speed rail. Following a review of time allocation theories, a time allocation model for general travel behavior is proposed as a further elaboration of Evans’ (1972) activities analysis. There are relationships among activities that can be expressed using a linear inequality to show the constraints on the arrangement of activities. This model indicates that two or more activities can be simultaneously rearranged to improve time management, which may be a source of variation in VTTS. This time allocation model can explain why large-scale high-speed rail construction in China faces significant market risks and a high likelihood of economic loss. Data from a new ticket sales and booking system for railway passengers indicate that passengers prefer conventional overnight sleeper trains, rather than high-speed trains, for long-distance travel, which supports the analysis of the time allocation model.     

Exploring passenger anxiety associated with train travel

Although people are often encouraged to use public transportation, the riding experience is not always comfortable. This study uses service items to measure passenger anxieties by applying a conceptual model based on the railway passenger service chain perspective. Passenger anxieties associated with train travel are measured using a modern psychometric method, the Rasch model. This study surveys 412 train passengers. Analytical results indicate that the following service items cause passenger anxiety during trains travel: crowding, delays, accessibility to a railway station, searching for the right train on a platform, and transferring trains. Empirical results obtained using the Rasch approach can be used to derive an effective strategy to reduce train passenger anxiety. This empirical study also demonstrates that anxiety differs based on passenger sex, age, riding frequency, and trip type. This information will also prove useful for transportation planners and policy-makers when considering the special travel needs of certain groups to create a user-friendly railway travel environment that promotes public use.     

A bi-level model for single-line rail timetable design with consideration of demand and capacity

This paper proposes a bi-level model to solve the timetable design problem for an urban rail line. The upper level model aims at determining the headways between trains to minimize total passenger cost, which includes not only the usual perceived travel time cost, but also penalties during travel. With the headways given by the upper level model, passengers’ arrival times at their origin stops are determined by the lower level model, in which the cost-minimizing behavior of each passenger is taken into account. To make the model more realistic, explicit capacity constraints of individual trains are considered. With these constraints, passengers cannot board a full train, but wait in queues for the next coming train. A two-stage genetic algorithm incorporating the method of successive averages is introduced to solve the bi-level model. Two hypothetical examples and a real world case are employed to evaluate the effectiveness of the proposed bi-level model and algorithm. Results show that the bi-level model performs well in reducing total passenger cost, especially in reducing waiting time cost and penalties. And the section loading-rates of trains in the optimized timetable are more balanced than the even-headway timetable. The sensitivity analyses show that passenger’s desired arrival time interval at destination and crowding penalty factor have a high influence on the optimal solution. And with the dispersing of passengers' desired arrival time intervals or the increase of crowding penalty factor, the section loading-rates of trains become more balanced.     

A delay root cause discovery and timetable adjustment model for enhancing the punctuality of railway services

Knock-on delay, which is the key factor in punctuality of railway service, is mainly related to two factors including the quality of timetable in the planning phase and disturbances which may result in unscheduled trains’ waiting or meeting in operation phase. If the delay root cause and the interactions among the factors responsible for these can be clearly clarified, then the punctuality of railway operations can be enhanced by taking reactions such as timetable adjustment, rescheduling or rerouting of railway traffic in case of disturbances. These delay reasons can be used to predict the lengths of railway disruptions and effective reactions can be applied in disruption management. In this work, a delay root cause discovery model is proposed, which integrates heterogeneous railway operation data sources to reconstruct the details of the railway operations. A supervised decision tree method following the machine learning and data mining techniques is designed to estimate the key factors in knock-on delays. It discovers the root cause delay factor by logically analyzing the scheduled or un-scheduled trains meetings and overtaking behaviors, and the subsequent delay propagations. Experiment results show that the proposed decision tree can predict the delay reason with the accuracy of 83%, and it can be further enhance to 90% if the delay cause is only considered “prolonged passengers boarding” and “meeting or overtaking” factors. The delay root cause can be discovered by the proposed model, verified by frequency filtering in operation records, and resolved by the adjustment of timetable which is an important reference for the next timetable rescheduling. The results of this study can be applied to railway operation decision support and disruption management, especially with regard to timetable rescheduling, trains resequencing or rerouting, system reliability analysis, and service quality improvements.     

A behavioural comparison of route choice on metro networks: Time,transfers, crowding,topology and socio-demographics

Understanding travellers’ behaviour is key element in transportation planning. This article presents a route choice model for metro networks that considers different time components as well as variables related to the transferring experience, train crowding, network topology and socio-demographic characteristics. The route choice model is applied to the London Underground and Santiago Metro networks, to make a comparison of the decision making process of the users on both cities. As all the variables are statistically significant, it is possible to affirm that public transport users take into account a wide variety of elements when choosing routes. While in London the travellers prefer to spend time walking, in Santiago is preferable to spend time waiting. Santiago Metro users are more willing to travel in crowded trains than London Underground users. Both user groups have a similar dispreference to transfers after controlling for the time spent on transfer, but different attitudes to ascending and descending transfers. Topological factors presented on a distorted Metro map are more important than actual topology to passengers’ route choice decisions.     

On the use of “average delay” as a measure of train reliability

We investigate how passengers on long-distance trains value unexpected delays relative to scheduled travel time and travel cost. For scheduled services with high reliability and long headways, the value of delays is most commonly assumed to be proportional to the average delay. By exploring how the valuation of train delays depends on delay risk and delay length, using three different stated choice data sets, we find that the “average delay” approach does not hold: the disutility increases slower than linearly in the delay risk. This means that using the average delay as a performance indicator, a guide for operations planning or for investment appraisal will underestimate the value of small risks of long delays relative to large risks for short delays. It also means that estimated valuations of “average delay” will depend on the delay risk level: valuations will be higher the lower the risk levels in the study are.     

Crowding in public transport systems: Effects on users,operation and implications for the estimation of demand

The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers’ wellbeing, valuation of waiting and in-vehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components (EC) models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. More research is needed to explore if these findings hold with more complex choice models and in other contexts.     

Frequency-based transit assignment considering seat capacities

This paper proposes a frequency-based assignment model that considers travellers probability of finding a seat in their perception of route cost and hence also their route choice. The model introduces a “fail-to-sit” probability at boarding points with travel costs based on the likelihood of travelling seated or standing. Priority rules are considered; in particular it is assumed that standing on-board passengers will occupy any available seats of alighting passengers before newly boarding passengers can fill any remaining seats. At the boarding point passengers are assumed to mingle, meaning that FIFO is not observed, as is the case for many crowded bus and metro stops, particularly in European countries. The route choice considers the common lines problem and an user equilibrium solution is sought through a Markov type network loading process and the method of successive averages. The model is first illustrated with a small example network before being applied to the inner zone of London’s underground network. The effect of different values passengers might attach to finding a seat are illustrated. Applications of the model for transit planning as well as for information provision at the journey planner stage are discussed.     

Train rescheduling model with train delay and passenger impatience time in urban subway network

This paper considers the train rescheduling problem with train delay in urban subway network. With the objective of minimizing the negative effect of train delay to passengers, which is quantified with a weighted combination of travel time cost and the cost of giving up the planned trips, train rescheduling model is proposed to jointly synchronize both train delay operation constraints and passenger behavior choices. Space–time network is proposed to describe passenger schedule‐based path choices and obtain the shortest travel times. Impatience time is defined to describe the intolerance of passengers to train delay. By comparing the increased travel time due to train delay with the passenger impatience time, a binary variable is defined to represent whether the passenger will give up their planned trips or not. The proposed train rescheduling model is implemented using genetic algorithm, and the model effectiveness is further examined through numerical experiments of real‐world urban subway train timetabling test. Duration effects of the train delay to the optimization results are analyzed. Copyright © 2017 John Wiley & Sons, Ltd.     

Optimal coordination of public-transit vehicles using operational tactics examined by simulation

This work focuses on improving transit-service reliability by optimally reducing the transfer time required in the operations of transit networks. Service reliability of public-transit operations is receiving increased attention as agencies are faced with immediate problems of proving credible service while attempting to reduce operating cost. Unreliable service has also been cited as the major deterrent to existing and potential passengers. Due to the fact that most of the public transit attributes are stochastic: travel time, dwell time, demand, etc., the passenger is likely to experience unplanned waiting times and ride times. One of the main components of service reliability is the use of transfers. Transfers have the advantages of reducing operational costs and introducing more flexible and efficient route planning. However its main drawback is the inconvenience of traveling multi-legged trips. This work introduces synchronized (timed) time-tables to diminish the waiting time caused by transfers. Their use, however, suffers from uncertainty about the simultaneous arrival of two (or more) vehicles at an existing stop. In order to alleviate the uncertainty of simultaneous arrivals, operational tactics such as hold, skip stop and short-turn can be deployed considering the positive and negative effects, of each tactic, on the total travel time. A dynamic programming model was developed for minimizing the total travel time resulting with a set of preferred tactics to be deployed. This work describes the optimization model using simulation for validation of the results attained. The results confirm the benefits of the model with 10% reduction of total travel time and more than 200% increase of direct transfers (transfers in which both vehicles arrive simultaneously to the transfer point).     

Transit network design based on travel time reliability

This paper presents a transit network optimization method, in which travel time reliability on road is considered. A robust optimization model, taking into account the stochastic travel time, is formulated to satisfy the demand of passengers and provide reliable transit service. The optimization model aims to maximize the efficiency of passenger trips in the optimized transit network. Tabu search algorithm is defined and implemented to solve the problem. Then, transit network optimization method proposed in this paper is tested with two numerical examples: a simple route and a medium-size network. The results show the proposed method can effectively improve the reliability of a transit network and reduce the travel time of passengers in general.     

Scheduling heterogeneous train traffic on double tracks with efficient dispatching rules

To further improve the utilization rate of railway tracks and reduce train delays, this paper focuses on developing a high-efficiency train routing and timetabling approach for double-track railway corridors in condition that trains are allowable to travel on reverse direction tracks. We first design an improved switchable policy which is rooted in the approaches by Mu and Dessouky (2013), with the analysis of possible delays caused by different path choices. Then, three novel integrated train routing and timetabling approaches are proposed on the basis of a discrete event model and different dispatching rules, including no switchable policy (No-SP), Mu and Dessouky (2013)’s switchable policy (Original-SP) and improved switchable policy (Improved-SP). To demonstrate the performance of the proposed approaches, the heterogeneous trains on Beijing–Shanghai high speed railway are scheduled by aforementioned approaches. The case studies indicate that in comparison to No-SP and Original-SP approaches, respectively, the Improved-SP approach can reduce the total delay of trains up to 44.44% and 73.53% within a short computational time. Moreover, all of the performance criteria of the Improved-SP approach are usually better than those of other two approaches.     

What do commuters think travel time reliability is worth? Calculating economic value of reducing the frequency and extent of unexpected delays

The measurement of transportation system reliability has become one of the central topics of travel demand studies. A growing literature concerns the measurement of value of travel time reliability which provides a monetary cost of avoiding unpredictable travel time. The goal of this study is to measure commuters’ sensitivities to travel time reliability and their willingness to pay (WTP) to avoid unreliable routes. The preferences are elicited through a pivoted stated preference survey technique. To circumvent the issue of presenting numerical distributions and statistical terms to day-to-day commuters, we use the frequency of delay days as a means of measuring traveler’s sensitivities to travel time reliability. The advantage of using simplified measures to elicit traveler preferences for travel time reliability is that these methods simply compare days with high delay to days with usual travel time. It was found that travelers are not only averse to the amount of unexpected delay but also to the frequency of days with unexpected delays. The paper presents WTP findings for three measures: travel time, frequency embedded travel time, and travel time reliability. The ‘reliability’ increase in WTP for travel time is found to be nearly proportional to the frequency of experiencing unexpected delays. For example, the WTP for mean travel time is calculated at $6.98/h; however, reliability adds $3.27 (about 50 % of $6.98) to avoid unexpected delays ‘5 out of 10 days’. The results of the study would provide valuable inputs to cost-benefit analyses and traffic and revenue studies required for road tolling investment projects.     

A stochastic optimization model for transit network timetable design to mitigate the randomness of traveling time by adding slack time

Transit network timetabling aims at determining the departure time of each trip of all lines in order to facilitate passengers transferring either to or from a bus. In this paper, we consider a bus timetabling problem with stochastic travel times (BTP-STT). Slack time is added into timetable to mitigate the randomness in bus travel times. We then develop a stochastic integer programming model for the BTP-STT to minimize the total waiting time cost for three types of passengers (i.e., transferring passengers, boarding passengers and through passengers). The mathematical properties of the model are characterized. Due to its computational complexity, a genetic algorithm with local search (GALS) is designed to solve our proposed model (OPM). The numerical results based on a small bus network show that the timetable obtained from OPM reduces the total waiting time cost by an average of 9.5%, when it is tested in different scenarios. OPM is relatively effective if the ratio of the number of through passengers to the number of transferring passengers is not larger than a threshold (e.g., 10 in our case). In addition, we test different scale instances randomly generated in a practical setting to further verify the effectiveness of OPM and GALS. We also find that adding slack time into timetable greatly benefits transferring passengers by reducing the rate of transferring failure.     

A simulation‐based reliability assessment approach for congested transit network

This paper is an attempt to develop a generic simulation‐based approach to assess transit service reliability, taking into account interaction between network performance and passengers' route choice behaviour. Three types of reliability, say, system wide travel time reliability, schedule reliability and direct boarding waiting‐time reliability are defined from perspectives of the community or transit administration, the operator and passengers. A Monte Carlo simulation approach with a stochastic user equilibrium transit assignment model embedded is proposed to quantify these three reliability measures of transit service. A simple transit network with a bus rapid transit (BRT) corridor is analysed as a case study where the impacts of BRT components on transit service reliability are evaluated preliminarily.     

The effect of travel time variability on route choice decision: a generalized linear mixed model based analysis

The effect of travel time variability (TTV) on route choice behavior is explored in this study. A stated preference survey is conducted to collect behavioral data on Shanghai drivers’ choice between a slow but stable route and a fast but unreliable route. Travel time and TTV are respectively measured by mean and standard deviation of random travel time. The generalized linear mixed model (GLMM) is applied to quantify trade-offs between travel time and TTV. The GLMM based route choice model effectively accounts for correlations among repeated observations from the same respondent, and captures heterogeneity in drivers’ values of TTV. Model estimation results show that, female drivers and drivers with rich driving experience are less likely to choose a route with high TTV; smaller expected travel time of a route increase the probability of its being chosen; all drivers have intrinsic preference for a route with smaller expected travel time, but the degree of preference may vary within the population; TTV on average has negative effects on route choice decision, but a small portion of drivers are risk-prone to choose a fast but unreliable route despite high TTV.     

New technology and the modeling of risk-taking behavior in congested road networks

Intelligent transport systems provide various means to improve traffic congestion in road networks. Evaluation of the benefits of these improvements requires consideration of commuters’ response to reliability and/or uncertainty of travel time under various circumstances. Various disruptions cause recurrent or non-recurrent congestion on road networks, which make road travel times intrinsically fluctuating and unpredictable. Confronted with such uncertain traffic conditions, commuters are known to develop some simple decision-making process to adjust their travel choices. This paper represents the decision-making process involved in departure-time and route choices as risk-taking behavior under uncertainty. An expected travel disutility function associated with commuters’ departure-time and route choices is formulated with taking into account the travel delay (due the recurrent congestion), the uncertainty of travel times (due to incident-induced congestion) and the consequent early or late arrival penalty. Commuters are assumed to make decision on the departure-time and route choices on the basis of the minimal expected travel disutility. Thus the network will achieve a simultaneous route and departure-time user equilibrium, in which no commuter can decrease his or her expected disutility by unilaterally changing the route or departure-time. The equilibrium is further formulated as an equivalent nonlinear complementarity problem and is then converted into an unconstrained minimization problem with the use of a gap function suggested recently. Two algorithms based on the Nelder–Mead multidimensional simplex method and the heuristic route/time-swapping approach, are adapted to solve the problem. Finally, numerical example is given to illustrate the application of the proposed model and algorithms.     

Transportation serviceability analysis for metropolitan commuting corridors based on modal choice modeling

Major commuting corridors in metropolitan areas generally comprise multiple transportation modes for commuters, such as transit (subways or buses), private vehicles, or park-and-ride combinations. During the morning peak hour, the commuters would choose one of the available transportation modes to travel through the corridors from rural/suburban living areas to urban working areas. This paper introduces a concept of transportation serviceability to evaluate a transportation mode’s service status in a specific link, route, road, or network during a certain period. The serviceability can be measured by the possibility that travelers choose a specific type of transportation service at a certain travel cost. The commuters’ modal-choice possibilities are calculated using a stochastic equilibrium model based on general travel cost. The modeling results illustrate how transportation serviceability is influenced by background traffic flow in a corridor, value of comfort for railway mode, and parking fee distribution.     

高铁站功能区布局优化模型研究

为使高铁站功能区的布局更加科学合理,论文以旅客在高铁站房内的平均走行时间最少和高铁站建造成本最低为目标,综合考虑功能区的面积、长宽比例、功能区禁止重叠、流线等约束条件,构建基于多目标混合规划的高铁站功能区布局优化模型,通过算例验证了模型的有效性。该方法得出的结论可以为高铁站功能区布局的规划设计提供参考。