Forecasting passenger travel demand — international aspects

The differing demand forecasting approaches adopted on a variety of European international travel studies are described in this paper. These include four models used on the studies of the Paris-Brussels-Cologne/Amsterdam high speed rail line, the model used in appraising the proposed Channel Tunnel rail services and the EC TASC model system. Contrasting features of these forecasting procedures are highlighted.     

Optimizing train stopping patterns and schedules for high-speed passenger rail corridors

High-speed railway (HSR) systems have been developing rapidly in China and various other countries throughout the past decade; as a result, the question of how to efficiently operate such large-scale systems is posing a new challenge to the railway industry. A high-quality train timetable should take full advantage of the system’s capacity to meet transportation demands. This paper presents a mathematical model for optimizing a train timetable for an HSR system. We propose an innovative methodology using a column-generation-based heuristic algorithm to simultaneously account for both passenger service demands and train scheduling. First, we transform a mathematical model into a simple linear programming problem using a Lagrangian relaxation method. Second, we search for the optimal solution by updating the restricted master problem (RMP) and the sub-problems in an iterative process using the column-generation-based algorithm. Finally, we consider the Beijing–Shanghai HSR line as a real-world application of the methodology; the results show that the optimization model and algorithm can improve the defined profit function by approximately 30% and increase the line capacity by approximately 27%. This methodology has the potential to improve the service level and capacity of HSR lines with no additional high-cost capital investment (e.g., the addition of new tracks, bridges and tunnels on the mainline and/or at stations).     

An aggregate demand model for intercity passenger travel in Sri Lanka

Inspite of the inherent weaknesses in aggregate demand models, they continue to be used in everyday applications, especially in developing countries. The largely data intensive disaggregate model preclude its application in many cases. This paper attempts the formulation and calibration of an aggregate total demand model for estimating inter-district passenger travel by public transport in Sri Lanka. In its process, an investigation is made of the common problems in the aggregate approach while examining possible remedial measures to improve the accuracy and (hence) the usability of the aggregate model. It is argued that commonly used variables and functional forms are inappropriate for making accurate estimates in developing countries. Consequently, the model calibration is shown to incorporate variables representing urbanisation, under-development, transfers, a mode-abstract cost function and intrinsic features. The necessity for functional form for each variable to be based on behavioral assumptions that are tested using the Box-Cox transformation for ensuring the best fit of the data is also observed. Although, the model form was calibrated for Sri Lanka, the model is generalised in order for its applications to other countries as well as, both, inter-district and intercity travel demand estimation.     

Structure and dynamics of non-suburban passenger travel demand in Indian railways

In this paper we derive long run structural relationships for all the three classes, viz. upper, second and ordinary second class, of non-suburban long distance passenger transport demand for Indian railways using annual time series data for 1970–1995. We employ some of the recent developments in multivariate dynamic econometric time series modeling including estimation of long-run structural cointegrating relationships, short-run dynamics and measurement of the effects of shocks and their persistence on evolution of the dynamic passenger transport demand system. The models are estimated using a cointegrating vector autoregressive (VAR) modeling framework, which allows for endogeneity of regressors. The demand systems are found to be stable for all the classes in the long run and they converge to equilibrium in a period of around 2–4 years after a typical system-wide shock. Any disequilibrium in the short-run is corrected in the long-run with adjustments in passenger transport demand and the price variable, i.e. real rate charged per passenger kilometer. Results show that travel demand in all classes would rise with income, although the rise is less than proportionate in the case of ordinary class. High price elasticity in long-run and short-run impulse responses indicate that passenger fare hike could lead to substantial decline in travel demand leading to decline in revenue earnings of the railways. This revised version was published online in June 2006 with corrections to the Cover Date.     

Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach

In a heavily congested metro line, unexpected disturbances often occur to cause the delay of the traveling passengers, infeasibility of the current timetable and reduction of the operational efficiency. Due to the uncertain and dynamic characteristics of passenger demands, the commonly used method to recover from disturbances in practice is to change the timetable and rolling stock manually based on the experiences and professional judgements. In this paper, we develop a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time and operational costs of trains. To capture the complexity of passenger traveling characteristics, the arriving ratio of passengers at each station is modeled as a non-homogeneous poisson distribution, in which the intensity function is treated as time-varying origin-to-destination passenger demand matrices. By considering the number of on-board passengers, the total energy usage is modeled as the difference between the tractive energy consumption and the regenerative energy. Then, we design an approximate dynamic programming based algorithm to solve the proposed model, which can obtain a high-quality solution in a short time. Finally, numerical examples with real-world data sets are implemented to verify the effectiveness and robustness of the proposed approaches.     

Derivation of train arrival timings through correlations from individual passenger farecard data

Transportation - In this paper, we propose a method for estimating the timings at which trains arrive and depart from stations using passenger farecard data and knowledge of the network topology....     

Simultaneous passenger train routing and timetabling using an efficient train-based Lagrangian relaxation decomposition

This paper focuses on the simultaneous passenger train routing and timetabling problem on the rail network consisting of both unidirectional and bidirectional tracks using an efficient train-based Lagrangian relaxation decomposition. We first build an integer linear programming model with many 0–1 binary and non-negative integer decision variables, after then reformulate it as a train path-choice model for providing an easier train-based Lagrangian relaxation decomposition mechanism based on the construction of space-time discretized network extending from node-cell-based rail network. Moreover, through reformulating safety usage interval restrictions with a smaller number of constraints in this reformulated model, the train-based decomposition needs fewer Lagrangian multipliers to relax these constraints. On the basis of this decomposition, a solving framework including a heuristic algorithm is proposed to simultaneously optimize both the dual and feasible solutions. A set of numerical experiments demonstrate the proposed Lagrangian relaxation decomposition approach has better performances in terms of minimizing both train travel time and computational times.     

Wanting to travel,more or less: Exploring the determinants of the deficit and surfeit of personal travel

This study investigates the determinants of peoples desire to increase or decrease the amount of travel they do. We use data from 1,357 working commuters, residents of three different neighborhoods in the San Francisco Bay Area, California. The dependent variables are indicators of Relative Desired Mobility for ten categories of travel (short- and long-distance overall and by several mode- and purpose-specific categories). These variables are measured on a five-point ordinal scale ranging from much less to much more, through which the respondents indicated the amount of travel they want to do (in the category in question) compared to what they are doing now. Censored ordered probit models were developed for these variables, with explanatory variables including general travel attitudes, specific liking for travel in each of the same separate categories, objective and subjective measures of the amount currently traveled in each category, and personality, lifestyle, and socio-demographic characteristics. The results support the hypotheses that the liking for travel has a strong positive impact, and subjective qualitative assessments of mobility have a strong negative impact, on the desire to increase ones travel. Finally, a number of general types of effects on Relative Desired Mobility were identified, among them complementarity and substitution effects. The results of this study can provide policy makers and researchers with new and valuable insight into key principles that affect individual travel demand.     

Reducing the passenger travel time in practice by the automated construction of a robust railway timetable

Automatically generating timetables has been an active research area for some time, but the application of this research in practice has been limited. We believe this is due to two reasons. Firstly, some of the models in the literature impose artificial upper bounds on time supplements. This causes a high risk of generating infeasibilities. Secondly, some models that leave out these upper bounds often generate solutions that contain some very large time supplements because these supplements are not penalised in the objective function. The reason is that these objective functions often do not completely correspond to the true goal of a timetable. We solve both problems by minimising our objective function: total passenger travel time, expected in practice. Since this function evaluates and indirectly steers all time related decision variables in the system, we do not need to further restrict the ranges of any of these variables. As a result, our model does not suffer from infeasibilities generated by such artificial upper bounds for supplements.Furthermore, some measures are taken to significantly speed up the solver times of our model. These combined features result in our model being solved more quickly than previous models. As a result, our method can be used for timetabling in practice. We demonstrate our claims by optimising, in about two hours only, the timetable of all 196 hourly passenger trains in Belgium. Assuming primary delay-distributions with an average of 2% on the minima of each activity, the optimised timetable reduces expected passenger time in practice, as evaluated on the macroscopic level, by 3.8% during peak hours. This paper demonstrates that we added two important missing steps to make cyclic timetabling for passengers really useable in practice: (i) the addition of the objective function of expected passenger time in practice and (ii) the reduction of computation time by addition of well chosen additional constraints.     

Calibrating travel time thresholds with cluster analysis and AFC data for passenger reasonable route generation on an urban rail transit network

Transportation - Estimating the route choice patterns for transit passengers is important to improve service reliability. The size and composition of a route choice set affects the choice model...     

Validation of a multimodal travel simulator with travel information provision

This paper presents a computer based travel simulator for collecting data concerning the use of next-generation ATIS and their effects on traveler decision making in a multimodal travel environment. The tool distinguishes itself by presenting a completely abstract multimodal transport network, where knowledge levels are fully controlled for in terms of awareness of mode-route combinations as well as in terms of variability of characteristics of known alternatives. Furthermore, it contains an information service-module that provides a variety of advanced types of travel information, with controlled for levels of reliability. These novel features warrant an extensive validation of the travel simulator. Such a validation is performed, using two datasets (one with revealed data, one with data from an experiment held in the simulator) obtained among 264 participants. Results suggest that the simulator succeeds in collecting useful data on multimodal travel choice making under provision of advanced types of travel information.     

An investigation of passenger interchange and train standing time at LRT stations: (ii) Estimation of standing time

The standing-time of trains at urban rail stations is pertinent to determining the line capacity and fleet size. The assumption of uniform boarding and alighting leads to under-estimation of the standing time. It is shown that the train standing-time is related to the fraction of boarders and the maximum demand for boarding and alighting at a door. It is further shown that the probability distribution of passengers at a door depends on the platform entrance locations. A methodology that takes into account the above factors is proposed for estimating the train standing-time.     

Experiments with an activity-based travel model

Transportation - Experiments are described with an activity-based travel model, estimated on a 7-day activity-diary survey. The first part of the paper describes the model system in its final form,...     

Subjective well-being related to satisfaction with daily travel

Previous research demonstrates an impact on subjective well-being (SWB) of affect associated with routine performance of out-of-home activities. A primary aim of the present study is to investigate whether satisfaction with daily travel has a positive impact on SWB, either directly or indirectly through facilitating the performance of out-of-home activities. A secondary aim is to determine whether emotional-symbolic or instrumental reasons for car use results in higher satisfaction with daily travel than other travel modes. A survey of a population-based sample of 1,330 Swedish citizens included measures of car access and use, satisfaction with daily travel, satisfaction with performance of out-of-home routine activities, and affective and cognitive SWB. The results confirmed that the effect on affective and cognitive SWB of satisfaction with daily travel is both direct and indirect via satisfaction with performance of activities. Percent weekly car use had a small effect on satisfaction with daily travel and on affective SWB, although fully mediating the effect of satisfaction with performance of the activities. This suggests that car use plays a minor role for satisfaction with daily travel and its effect on SWB. This role may be larger if investigated after a forced reduced car use.     

Distribution-free travel time reliability assessment with probability inequalities

An assumption that pervades the current transportation system reliability assessment literature is that probability distributions of the sources of uncertainty are known explicitly. However, this distribution may be unavailable (inaccurate) in reality as we may have no (insufficient) data to calibrate the distribution. In this paper we relax this assumption and present a new method to assess travel time reliability that is distribution-free in the sense that the methodology only requires that the first N moments (where N is a user-specified positive integer) of the travel time to be known and that the travel times reside in a set of bounded and known intervals. Because of our modeling approach, all sources of uncertainty are automatically accounted for, as long as they are statistically independent. Instead of deriving exact probabilities on travel times exceeding certain thresholds via computationally intensive methods, we develop semi-analytical probability inequalities to quickly (i.e. within a fraction of a second) obtain upper bounds on the desired probability. Numerical experiments suggest that the inclusion of higher order moments can potentially significantly improve the bounds. The case study also demonstrates that the derived bounds are nontrivial for a large range of travel time values.     

An investigation of passenger interchange and train standing time at LRT stations: (i) Alighting,boarding and platform distribution of passengers

The train standing-time at a station is a determinant of the line capacity and the necessary fleet-size. Its determination is usually based on the assumption that boarding and alighting is uniform at all doors of a train. Uniform boarding and alighting is conceivable if passengers distribute themselves uniformly on station platforms while waiting for trains. The validity of the uniformity assumptions is tested using data from two stations (one CBD, one suburban) of the Calgary, Alberta LRT system. It is shown that passenger distribution on the platform, alighting and boarding is not uniform and is closely related to the location of platform access points. Some strategies that will encourage uniformity are discussed. However, procedures that can estimate the standing time for non-uniform boarding and alighting need to be developed.     

Behavioural implications of preferences, risk attitudes and beliefs in modelling risky travel choice with travel time variability

The appropriate interpretation of a behavioural outcome requires allowing for risk attitude and belief of an individual, in addition to identification of preferences. This paper develops an Attribute-Specific Extended Rank-Dependent Utility Theory model to better understand choice behaviour in the presence of travel time variability, in which these three important components of choice are empirically addressed. This framework is more behaviourally appealing for travel time and travel time variability research than the traditional approach in which risk attitude and belief are overlooked. This model also reveals significant unobserved between-individual heterogeneity in preferences, risk attitudes and beliefs.     

The interactions between online shopping and personal activity travel behavior: an analysis with a GPS-based activity travel diary

Accompanying the widespread use of the Internet, the popularity of e-commerce is growing in developing countries such as China. Online shopping has significant effects on in-store shopping and on other personal activity travel behavior such as leisure activities and trip chaining behavior. Using data collected from a GPS-based activity travel diary in the Shangdi area of Beijing, this paper investigates the relationships between online shopping, in-store shopping and other dimensions of activity travel behavior using a structural equation modelling framework. Our results show that online buying frequency has positive effects on the frequencies of both in-store shopping and online searching, and in-store shopping frequency positively affects the frequency of online searching. Frequent online purchasers tend to shop in stores on weekends rather than weekdays. We also found a negative effect of online buying on the frequency of leisure activities, indicating that online shopping may reduce out-of-home leisure trips.     

Multiple-phase train trajectory optimization with signalling and operational constraints

The train trajectory optimization problem aims at finding the optimal speed profiles and control regimes for a safe, punctual, comfortable, and energy-efficient train operation. This paper studies the train trajectory optimization problem with consideration of general operational constraints as well as signalling constraints. Operational constraints refer to time and speed restrictions from the actual timetable, while signalling constraints refer to the influences of signal aspects and automatic train protection on train operation. A railway timetable provides each train with a train path envelope, which consists of a set of positions on the route with a specified target time and speed point or window. The train trajectory optimization problem is formulated as a multiple-phase optimal control model and solved by a pseudospectral method. This model is able to capture varying gradients and speed limits, as well as time and speed constraints from the train path envelope. Train trajectory calculation methods under delay and no-delay situations are discussed. When the train follows the planned timetable, the train trajectory calculation aims at minimizing energy consumption, whereas in the case of delays the train trajectory is re-calculated to track the possibly adjusted timetable with the aim of minimizing delays as well as energy consumption. Moreover, the train operation could be affected by yellow or red signals, which is taken into account in the train speed regulation. For this purpose, two optimization policies are developed with either limited or full information of the train ahead. A local signal response policy ensures that the train makes correct and quick responses to different signalling aspects, while a global green wave policy aims at avoiding yellow signals and thus proceed with all green signals. The method is applied in a case study of two successive trains running on a corridor with various delays showing the benefit of accurate predictive information of the leading train on energy consumption and train delay of the following train.