Dynamic models of commuter behavior: Experimental investigation and application to the analysis of planned traffic disruptions

This paper reviews the results of a series of experiments aimed at investigating the day-to-day dynamics of commuter behavior in congested traffic systems. The interactive experiments involve actual work commuters in a simulated traffic system, whereby commuters noncooperatively supply their decisions to a traffic simulation model that determines the resulting arrival times and associated trip times; these in turn form the basis of the commuters' decisions on the next day. Models are developed to predict the daily switching of departure time and/or route by individual commuters in response to experienced congestion in the system or to exogenously supplied information. These models are incorporated in a dynamic modelling framework for the analysis of the impacts of planned traffic disruptions, such as those associated with major highway repair and reconstruction activities.     

The evening commute with cars and transit: Duality results and user equilibrium for the combined morning and evening peaks

This paper extends Vickrey’s (1969) commute problem for commuters wishing to pass a bottleneck for both cars and transit that share finite road capacity. In addition to this more general framework considering two modes, the paper focuses on the evening rush, when commuters travel from work to home. Commuters choose which mode to use and when to travel in order to minimize the generalized cost of their own trips, including queueing delay and penalties for deviation from a preferred schedule of arrival and departure to and from work. The user equilibrium for the isolated morning and evening commutes are shown to be asymmetric because the schedule penalty in the morning is the difference between the departure and wished curves, and the schedule penalty in the evening is the difference between the arrival and wished curves. It is shown that the system optimum in the morning and evening peaks are symmetric because queueing delay is eliminated and the optimal arrival curves are the same as the departure curves.The paper then considers both the morning and evening peaks together for a single mode bottleneck (all cars) with identical travelers that share the same wished times. For a schedule penalty function of the morning departure and evening arrival times that is positive definite and has certain properties, a user equilibrium is shown to exist in which commuters travel in the same order in both peaks. The result is used to illustrate the user equilibrium for two cases: (i) commuters have decoupled schedule preferences in the morning and evening and (ii) commuters must work a fixed shift length but have flexibility when to start. Finally, a special case is considered with cars and transit: commuters have the same wished order in the morning and evening peaks. Commuters must use the same mode in both directions, and the complete user equilibrium solution reveals the number of commuters using cars and transit and the period in the middle of each rush when transit is used.     

Using ordered probit modeling to study the effect of ATIS on transit ridership

A computer-aided telephone interview was conducted in two metropolitan areas in northern California. The survey included an innovative stated preference design to collect data that address the potential of advanced transit information systems. The study’s main objectives are to investigate whether advanced transit information would increase the acceptance of transit, and to determine the types and levels of information that are desired by commuters. The survey included a customized procedure that presents realistic choice sets, including the respondent’s preferred information items and realistic travel times. The ordered probit modeling technique was used. The results indicated a promising potential of advanced transit information in increasing the acceptance of transit as a commute mode. It also showed that the frequency of service, number of transfers, seat availability, walking time to the transit stop and fare information are among the significant information types that commuters desire. Commute time by transit, income, education, and whether the commuter is currently carpooling, were among the factors that contribute to the likelihood of using transit given information was provided.     

A latent class joint mode and departure time choice model for the Greater Toronto and Hamilton Area

This paper presents a closed-form Latent Class Model (LCM) of joint mode and departure time choices. The proposed LCM offers compound substitution patterns between the two choices. The class-specific choice models are of two opposing nesting structures, each of which provides expected maximum utility feedback to the corresponding class membership model. Such feedback allows switching class membership in response to the changes in choice contexts. The model is used for an empirical investigation of commuting mode and departure time choices in the Greater Toronto and Hamilton Area (GTHA) by using a large sample household travel survey dataset. The empirical model reveals that overall 38% of the commuters in the GTHA are more likely to switch modes than departure times and 62% of them are more likely to do the reverse. The empirical model also reveals that the average Subjective Value of Travel Time Savings (SVTTS) of the commuters in the GTHA can be as low as 3 dollars if a single choice pattern of departure time choices nested within mode choices is considered. It can also be as high as 67 dollars if the opposite nesting structure is assumed. However, the LCM estimates the average SVTTS to be around 27 dollars in the GTHA. An empirical scenario analysis by using the estimated model indicates that a 50% increase in morning peak period car travel time does not sway more than 4% of commuters from the morning peak period.

     

Influences on commuter trip departure time decisions in Singapore

This paper provides empirical evidence to support the widely held view that institutional factors such as official work start times and staggered working hours are powerful policy tools in traffic management and in influencing travel behaviour. This approach is to be preferred over continued investment in infrastructure given the scarcity of land in Singapore. A more efficient use of existing infrastructure could be achieved by spreading peak travel. Full utilisation of the Mass Rapid Transit will depend on changing the commuter's perception on multi mode travel in addition to using public transport. While many studies have been carried out on modal choice, research on commuter trip departure decisions have been few and remain largely least understood. This paper employs multinomial logit and simultaneous nested logit analysis to model the choice of departure time (using household data collected in Singapore in 1983). Preliminary findings show that schedule delay, travel cost, and journey time to be important influences on commuter's choice of trip departure time to work. Some difficulties are highlighted and suggestions for further research are made.     

A procedure for estimating transit subsidization requirements for developing countries

The paper presents a procedure that has been developed for estimating subsidization requirements for urban transit services in developing countries. The procedure is based on a subsidization policy of reducing transport expenditure burden on the average commuter, by maintaining his transport expenditure-income ratio at a reasonable level. It is designed for both regulated and deregulated transport markets. It requires, as input, historical data (previous year) on fare, productivity, and load factor for the transport service or mode concerned, the transport expenditure-income ratio distribution of the commuters, and the current level of commuter personal transport allowance. It is based on the premise that transport expenditure-income ratio is inversely related to income. The subsidization formula developed in the paper yields a level of subsidy that is commensurate with the level of control a government is able to exercise over transit operations.     

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.     

Adaptive traffic signal control with equilibrium constraints under stochastic demand

This study develops a methodology to model transportation network design with signal settings in the presence of demand uncertainty. It is assumed that the total travel demand consists of commuters and infrequent travellers. The commuter travel demand is deterministic, whereas the demand of infrequent travellers is stochastic. Variations in demand contribute to travel time uncertainty and affect commuters’ route choice behaviour. In this paper, we first introduce an equilibrium flow model that takes account of uncertain demand. A two-stage stochastic program is then proposed to formulate the network signal design under demand uncertainty. The optimal control policy derived under the two-stage stochastic program is able to (1) optimize the steady-state network performance in the long run, and (2) respond to short-term demand variations. In the first stage, a base signal control plan with a buffer against variability is introduced to control the equilibrium flow pattern and the resulting steady-state performance. In the second stage, after realizations of the random demand, recourse decisions of adaptive signal settings are determined to address the occasional demand overflows, so as to avoid transient congestion. The overall objective is to minimize the expected total travel time. To solve the two-stage stochastic program, a concept of service reliability associated with the control buffer is introduced. A reliability-based gradient projection algorithm is then developed. Numerical examples are performed to illustrate the properties of the proposed control method as well as its capability of optimizing steady-state performance while adaptively responding to changing traffic flows. Comparison results show that the proposed method exhibits advantages over the traditional mean-value approach in improving network expected total travel times.     

Modeling the impact of pre-trip information on commuter departure time and route choice

The objective of this paper is to investigate the impact of pre-trip information on auto commuters’ choice behavior. The analysis is based on an extensive home-interview survey of commuters in the Taichung metropolitan area in Taiwan. A joint model for route and departure time decisions with and without pre-trip information is formulated. The model specifications are developed for both the systematic and random components. In particular, econometric issues associated with specifying the random error structure are addressed for parameter estimation purposes. Insights into the effects of attributes are obtained through the analysis of the model's performance and estimated parameter values. A probit model form is used for the joint model, allowing the introduction of state dependence and correlation in the model specification. The results underscore the important relationship between the different characteristics and the propensity of commuter choice behavior under two scenarios, with and without pre-trip information.     

Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach

This paper assesses the demand for a flexible, demand-adaptive transit service, using the Chicago region as an example. We designed and implemented a stated-preference survey in order to (1) identify potential users of flexible transit, and (2) inform the service design of the flexible transit mode. Multinomial logit, mixed-logit, and panel mixed-logit choice models were estimated using the data obtained from the survey. The survey instrument employed a d_p-efficient design and the Google Maps API to capture precise origins and destinations in order to create realistic choice scenarios. The stated-preference experiments offered respondents a choice between traditional transit, car, and a hypothetical flexible transit mode. Wait time, access time, travel time, service frequency, cost, and number of transfers varied across the choice scenarios. The choice model results indicate mode-specific values of in-vehicle travel time ranging between $16.3 per hour (car) and $21.1 per hour (flexible transit). The estimated value of walking time to transit is $25.9 per hour. The estimated value of waiting time at one’s point of origin for a flexible transit vehicle is $11.3 per hour; this value is significantly lower than the disutility typically associated with waiting at a transit stop/station indicating that the ‘at-home’ pick-up option of flexible transit is a highly desirable feature. The choice model results also indicate that respondents who use active-transport modes or public transit for their current commute trip, or are bikeshare members, were significantly more likely to choose flexible and traditional transit than car commuters in the choice experiments. The implications of these and other relevant model results for the design and delivery of flexible, technology-enabled services are discussed.     

Traffic rationing and pricing in a linear monocentric city

This paper presents a simple spatial equilibrium model for a linear monocentric city to investigate the effects of rationing and pricing on morning commuters' travel cost and modal choice behavior in each location. Under rationing and pricing, every day in the morning peak hour, each commuter is classified as either “free” or “rationed”. “Free” commuters are allowed to use the highway without paying the toll, whereas “rationed” commuters can avoid the toll only if they travel by transit. Each day, a fraction of commuters are rationed in their free use of the highway, and the rationing fractions are determined systematically so that everyone is equally rationed in a given period. It is found that Pareto‐improving rationing and pricing scheme might be obtained as a combination of the rationing degree and the toll associated with rationing. Extension to the rationing and pricing scheme with cordon and park‐and‐ride service has been made. Cordon and park‐and‐ride might help in improving the efficiency of rationing and pricing strategy although remains its Pareto‐improving property. Copyright © 2012 John Wiley & Sons, Ltd.     

Modeling the commute mode share of transit using continuous accessibility to jobs

This paper presents the results of an accessibility-based model of aggregate commute mode share, focusing on the share of transit relative to auto. It demonstrates the use of continuous accessibility – calculated continuously in time, rather than at a single of a few departure times – for the evaluation of transit systems. These accessibility calculations are accomplished using only publicly-available data sources. A binomial logic model is estimated which predicts the likelihood that a commuter will choose transit rather than auto for a commute trip based on aggregate characteristics of the surrounding area. Variables in this model include demographic factors as well as detailed accessibility calculations for both transit and auto. The mode achieves a ρ² value of 0.597, and analysis of the results suggests that continuous accessibility of transit systems may be a valuable tool for use in modeling and forecasting.     

Accounting for systematic heterogeneity across car commuters in response to multiple TDM policies: case study of Tehran

Modeling commuters’ choice behavior in response to transportation demand management (TDM) helps in predicting the consequences of TDM policies. Although research looking at choice behavior has evolved to investigate preference heterogeneity in response to factors influencing mode choice, as far as we know, no study has considered taste variation across commuters in response to multiple TDM policies. This paper investigates the presence of systematic preference heterogeneity across commuters, in response to the TDM policies that can be explained by their socio-economic or commuting-related characteristics. Analysis is based on results of a stated preference survey developed using a Design of Experiments approach. Five policies were assessed in order to study the impact they had on how commuters chose their mode of transportation. These include increasing parking cost, increasing fuel cost, implementing cordon pricing, reducing transit time and improving access to transit facilities. For the sake of assessing both systematic and random preference heterogeneity across car commuters, a form of the Mixed Multinomial Logit (MMNL) model that identifies sources of heterogeneity and consequently makes the choice models less restrictive in considering both systematic and random preference variation across individuals was developed. The sample includes 366 individuals who regularly commute to their workplace in the city center of Tehran, Iran. The likelihood function value of this model shows a significant improvement compared to the base MNL model, using the same variables. The MMNL model shows that taste variation across the studied commuters results in differences in influences estimated for three policies: increasing parking cost, reducing transit time and improving access to transit. The analysis examines several distributions for random parameters to test the impacts of restricting distributions to allow for only normality. The results confirm the potential to improve model fit with alternative distributions.     

Estimating commuter mode choice: A discrete choice analysis of the impact of road pricing and parking charges

Automobile use leads to external costs associated with emissions, congestion, noise and other impacts. One option for minimizing these costs is to introduce road pricing and parking charges to reduce demand for single occupant vehicle (SOV) use, while providing improvements to alternatives to encourage mode switching. However, the impact of these policies on urban mode choice is uncertain, and results reported from regions where charging has been introduced may not be transferable. In particular, revealed preference data associated with cost recovery tolls on single facilities may not provide a clear picture of driver response to tolls for demand management. To estimate commuter mode choice behaviour in response to such policies, 548 commuters from a Greater Vancouver suburb who presently drive alone to work completed an individually customized discrete choice experiment (DCE) in which they chose between driving alone, carpooling or taking a hypothetical express bus service when choices varied in terms of time and cost attributes. Attribute coefficients identified with the DCE were used in a predictive model to estimate commuter response to various policy oriented combinations of charges and incentives. Model results suggest that increases in drive alone costs will bring about greater reductions in SOV demand than increases in SOV travel time or improvements in the times and costs of alternatives beyond a base level of service. The methods described here provide an effective and efficient way for policy makers to develop an initial assessment of driver reactions to the introduction of pricing policies in their particular regions.     

Behavioral adjustments and equity effects of congestion pricing: Analysis of morning commutes during the Stockholm Trial

This paper assesses the horizontal and vertical equity effects of the Stockholm Trial with Congestion Pricing for morning commuters, in terms of both travel behavioral adjustments and welfare effects, as a result of the toll’s direct effects and the behavioral adjustments. We consider specifically two behavioral adjustments: mode choice and departure time choice. Initial car drivers crossing the toll cordon had a 15 percentage-points higher rate of switching to public transit as compared with those not crossing the cordon. We also find some evidence of peak spreading, in particular toward a later departure time, as a result of the charging scheme, but most people choose a departure time within 15 min both before and during the trial. In the welfare analysis, we found no clear pattern of increasing burden by either increasing income or decreasing income, and the increase in the Gini Coefficient was insignificant. We also found no significant difference in either the mode-switching behavior or the average welfare effect for women versus for men.     

Learning-based framework for transit assignment modeling under information provision

The modeling of service dynamics has been the focus of recent developments in the field of transit assignment modeling. The emerging focus on dynamic service modeling requires a corresponding shift in transit demand modeling to represent appropriately the dynamic behaviour of passengers and their responses to Intelligent Transportation Systems technologies. This paper presents the theoretical development of a departure time and transit path choice model based on the Markovian Decision Process. This model is the core of the MIcrosimulation Learning-based Approach to TRansit Assignment. Passengers, while traveling, move to different locations in the transit network at different points in time (e.g. at stop, on board), representing a stochastic process. This stochastic process is partly dependent on the transit service performance and partly controlled by the transit rider’s trip choices. This can be analyzed as a Markovian Decision Process, in which actions are rewarded and hence passengers’ optimal policies for maximizing the trip utility can be estimated. The proposed model is classified as a bounded rational model, with a constant utility term and a stochastic choice rule. The model is appropriate for modeling information provision since it distinguishes between individual’s experience with the service performance and information provided about system dynamics.     

Integrating commuter information needs in the design of a motorist information system

This paper presents the results of a large sample survey designed to investigate the response of commuters to the delivery of traffic information. The main purpose of the survey was to investigate the impact of traffic information on commuters route choices, mode choices, and departure times in order to provide functional requirements for the design of a real-time motorist information system. The surveyed population consisted of 3,893 freeway motorists who routinely commuted to a central business district. The results of the survey indicated that four distinct commuter subgroups existed with respect to their traffic information needs: These groups were: (1) route changers, those willing to change route, or mode before entering the freeway (20.6%), (2) nonchangers, those unwilling to change time, route, or mode (23.4%), (3) time and route changers, (40.1%), and (4) pretrip changers, those willing to change time, mode, or route before leaving the house (15.9%). In terms of receiving traffic information, commercial radio was rated as the most useful and preferred medium both before and while driving. However, only a small, discrete group of commuters were likely to be influenced to change transportation mode. Implication of the survey results for the design of a real-time motorist information system are discussed.     

An application-oriented model of passenger waiting time based on bus departure time intervals

This paper develops an application-oriented model to estimate waiting times as a function of bus departure time intervals. Bus stops are classified into Type A and B depending on whether they are connected with urban rail transit systems. Distributions of passenger arrival rates are analyzed based on field data for Beijing. The results indicate that the best fits for the distribution of passenger arrival rates for Type A and B bus stops are the lognormal distribution and gamma distribution, respectively. By analyzing relationships between passenger arrival rates and bus departure time intervals, it is demonstrated that parameters of the passenger arrival rate distribution can be expressed by the average and coefficient of variation of bus departure time intervals in functional relationships. The validation shows that the model provides a reliable estimation of the average passenger waiting time based on readily available bus departure time intervals.     

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).