An investigation of commuting trip timing and mode choice in the Greater Toronto Area: Application of a joint discrete-continuous model

The trip timing and mode choice are two critical decisions of individual commuters mostly define peak period traffic congestion in urban areas. Due to the increasing evidence in many North American cities that the duration of the congested peak travelling periods is expanding (peak spreading), it becomes necessary and natural to investigate these two commuting decisions jointly. In addition to being considered jointly with mode choice decisions, trip timing must also be modelled as a continuous variable in order to precisely capture peak spreading trends in a policy sensitive transportation demand model. However, in the literature to date, these two fundamental decisions have largely been treated separately or in some cases as integrated discrete decisions for joint investigation. In this paper, a discrete-continuous econometric model is used to investigate the joint decisions of trip timing and mode choice for commuting trips in the Greater Toronto Area (GTA). The joint model, with a multinomial logit model for mode choice and a continuous time hazard model for trip timing, allows for unrestricted correlation between the unobserved factors influencing these two decisions. Models are estimated by occupation groups using 2001 travel survey data for the GTA. Across all occupation groups, strong correlations between unobserved factors influencing mode choice and trip timing are found. Furthermore, the estimated model proves that it sufficiently captures the peak spreading phenomenon and is capable of being applied within the activity-based travel demand model framework.     

Time-dependent congestion pricing system for large networks: Integrating departure time choice,dynamic traffic assignment and regional travel surveys in the Greater Toronto Area

Congestion pricing is one of the widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a framework for large-scale variable congestion pricing policy determination and evaluation. The proposed framework integrates departure time choice and route choice models within a regional dynamic traffic assignment (DTA) simulation environment. The framework addresses the impact of tolling on: (1) road traffic congestion (supply side), and (2) travelers’ choice dimensions including departure time and route choices (demand side). The framework is applied to a simulation-based case study of tolling a major freeway in Toronto while capturing the regional effects across the Greater Toronto Area (GTA). The models are developed and calibrated using regional household travel survey data that reflect the heterogeneity of travelers’ attributes. The DTA model is calibrated using actual traffic counts from the Ontario Ministry of Transportation and the City of Toronto. The case study examined two tolling scenarios: flat and variable tolling. The results indicate that: (1) more benefits are attained from variable pricing, that mirrors temporal congestion patterns, due to departure time rescheduling as opposed to predominantly re-routing only in the case of flat tolling, (2) widespread spatial and temporal re-distributions of traffic demand are observed across the regional network in response to tolling a significant, yet relatively short, expressway serving Downtown Toronto, and (3) flat tolling causes major and counterproductive rerouting patterns during peak hours, which was observed to block access to the tolled facility itself.     

Longitudinal analysis of activity generation in the Greater Toronto and Hamilton Area

Transportation - This paper presents a longitudinal analysis of activity generation behaviour in the Greater Toronto and Hamilton Area (GTHA) between 1996 and 2016 for various activity types: work,...     

A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.     

A solution algorithm for a dynamic deterministic user equilibrium assignment model with departure time choice

Abstract

A route-based combined model of dynamic deterministic route and departure time choice and a solution method for many origin and destination pairs is proposed. The divided linear travel time model is used to calculate the link travel time and to describe the propagation of flow over time. For the calculation of route travel times, the predictive ideal route travel time concept is adopted. Solving the combined model of dynamic deterministic route and departure time choice is shown to be equivalent to solving simultaneously a system of non-linear equations. A Newton-type iterative scheme is proposed to solve this problem. The performance of the proposed solution method is demonstrated using a version of the Sioux Falls network. This shows that the proposed solution method produces good equilibrium solutions with reasonable computational cost.     

Factors underlying the connections between active transportation and public transit at commuter rail in the Greater Toronto and Hamilton Area

Transportation - Encouraging the integration of active transportation with transit is increasingly being pursued as a strategy by transit agencies to boost alternative means to access transit...     

The role of intention as mediator between latent effects and behavior: application of a hybrid choice model to study departure time choices

Transportation - An increasing number of papers are focusing on integrating psychological aspects into the typical discrete choice models. The majority of these studies account for several latent...     

How habit moderates the commute mode decision process: integration of the theory of planned behavior and latent class choice model

Transportation - This study attempts to develop a comprehensive framework by integrating the theory of planned behavior (TPB) and latent class choice model, with aim to understanding how mode-use...     

Heterogeneity in marginal value of urban mobility: evidence from a large-scale household travel survey in the Greater Toronto and Hamilton Area

Transportation - The value of mobility is an unresolved question in transportation economics literature. The advent of ride-hailing services and the emergence of mobility as a service (MaaS) place...     

Hierarchical ordering of nests in a joint mode and destination choice model

This paper seeks to explore the relationship between mode and destination choice in an integrated nested choice model. A fundamental argument can be made that in certain circumstances, the ordering of choices should be reversed from the usual sequence of destination choice preceding mode choice. This results in a travel demand model where travelers are more likely to change destinations than to change transportation modes. For small and medium size urban areas, particularly in the United States, with less well developed public transit systems that draw few choice riders, this assumption makes much more sense than the traditional modeling assumptions. The models used in the new travel modeling system developed for Knoxville, Tennessee utilize this reversed ordering, with generally good results, which required no external tinkering in the logsum parameters.     

A simultaneous destination and mode choice model for shopping trips

This paper describes a disaggregate simultaneous destination and mode choice model for shopping trips. Following an introduction to the model structure and a review of the data, the results of five different model specifications are discussed. The models were estimated using data from two communities adjacent to Eindhoven, the Netherlands and utilise the multinomial logit model.     

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.     

Implications of congestion charging for departure time choice: Work and non-work schedule flexibility

The objective of this paper is to investigate the potential impacts of implementing variable congestion charging on the peak spreading of departure time choices, taking into account levels of scheduling flexibility of individuals. In particular, this study addresses non-work activities as well as socio-economic characteristics and their influence on scheduling flexibility for work trips. Departure time choice models were calibrated using data collected as part of a larger survey on the consequences of congestion charging on travel choices in the city of Edinburgh. The inclusion of variables related to work and non-work scheduling, as well as socio-economic variables have improved the performance of the models. This suggests that non-work activities, as well as work schedule flexibility have an impact on departure time choice for the journey to work. This means that even for those with flexible work schedules, but with other non-work commitments, the timing of their work trip may not be so flexible. Therefore, for the success of variable congestion charging schemes, other complimentary measures should be introduced in parallel. These include, for example, child care provision at work, opening hours of shops and leisure facilities.     

A tour-based model of travel mode choice

This paper presents a new tour-based mode choice model. The model is agent-based: both households and individuals are modelled within an object-oriented, microsimulation framework. The model is household-based in that inter-personal household constraints on vehicle usage are modelled, and the auto passenger mode is modelled as a joint decision between the driver and the passenger(s) to ride-share. Decisions are modelled using a random utility framework. Utility signals are used to communicate preferences among the agents and to make trade-offs among competing demands. Each person is assumed to choose the best combination of modes available to execute each tour, subject to auto availability constraints that are determined at the household level. The households allocations of resources (i.e., cars to drivers and drivers to ride-sharing passengers) are based on maximizing overall household utility, subject to current household resource levels. The model is activity-based: it is designed for integration within a household-based activity scheduling microsimulator. The model is both chain-based and trip-based. It is trip-based in that the ultimate output of the model is a chosen, feasible travel mode for each trip in the simulation. These trip modes are, however, determined through a chain-based analysis. A key organizing principle in the model is that if a car is to be used on a tour, it must be used for the entire chain, since the car must be returned home at the end of the tour. No such constraint, however, exists with respect to other modes such as walk and transit. The paper presents the full conceptual model and estimation results for an initial empirical prototype. Because of the complex nature of the model decision structure, choice probabilities are simulated from direct generation of random utilities rather than through an analytical probability expression.     

A Stated preference freight mode choice model

Transport policy aims to assist the transport system to work more efficiently and effectively. An understanding of the reasons why people choose to move freight in a certain manner is critical to the development of appropriate policies. This article outlines a data collection approach and the development of a disaggregate mode choice model applicable to the analysis of freight shipper decision making. It focuses on the choice between rail and road in Java, Indonesia. The model indicates that safety, reliability and responsiveness are major attributes influencing rail/road freight mode choice. Transport policies aimed at improving these dimensions should increase the attractiveness of rail transport.     

Complementarity formulations for the cell transmission model based dynamic user equilibrium with departure time choice, elastic demand and user heterogeneity

In this paper we formulate the dynamic user equilibrium problem with an embedded cell transmission model on a network with a single OD pair, multiple parallel paths, multiple user classes with elastic demand. The formulation is based on ideas from complementarity theory. The travel time is estimated based on two methods which have different transportation applications: (1) maximum travel time and (2) average travel time. These travel time functions result in linear and non-linear complementarity formulations respectively. Solution existence and the properties of the formulations are rigorously analyzed. Extensive computational experiments are conducted to demonstrate the benefits of the proposed formulations on various test networks.     

A micro-simulation model system of departure time using a perception updating model under travel time uncertainty

Existing microscopic traffic models have often neglected departure time change as a possible response to congestion. In addition, they lack a formal model of how travellers base their daily travel decisions on the accumulated experience gathered from repetitively travelling through the transport network. This paper proposes an approach to account for these shortcomings. A micro-simulation approach is applied, in which individuals base their consecutive departure time decisions on a mental model. The mental model is the outcome of a continuous process of perception updating according to principles of reinforcement learning. Individuals’ daily travel decisions are linked to the traffic simulator SIAS-PARAMICS to create a simulation system in which both individual decision-making and system performance (and interactions between these two levels) are adequately represented. The model is applied in a case study that supports the feasibility of this approach.     

A comparison of the sustainability of public and private transportation systems: Study of the Greater Toronto Area

A macroscopic assessment of the impacts of private and public transportation systems on the sustainability of the Greater Toronto Area (GTA) is undertaken from economic, environmental and social perspectives. The methodology draws upon the urban metabolism and sustainability indicators approaches to assessing urban sustainability, but compares modes in terms of passenger-kms. In assessing the economic sustainability of a city, transportation should be recognized as a product, a driver and a cost. In 1993, the traded costs of automobile use in the GTA were approximately balanced by the value of the automobile parts and assembly industry. But local transit costs 1/3 to 1/6 of the auto costs per person-km, in traded dollars, mainly because local labour is the primary cost.Public transportation is more sustainable from an environmental perspective. Automobile emissions are a major contributor to air pollution, which is a serious contemporary environmental health problem in Toronto. Public transportation modes are less energy intensive (including indirect energy consumption) and produce CO₂ at an order of magnitude lower, although these benefits are partially undermined by under-utilization of transit capacity and the source of electricity generation.The social benefits of automobile use are likely more significant than costs in determining GTA residents' preferential mode choice. The speed and access of auto use provide important economic benefits, e.g. relating to employment and product choice. Nevertheless, offsetting the service attributes of private transportation are large social costs in terms of accidents. The costs of automobile insurance provide one tangible measure of such negative impacts.In order to improve the sustainability of the GTA, innovative approaches are required for improving the performance level of public transportation or substantially reducing the need for the service level provided by automobiles. Efforts such as greater integration of bicycles with public transit, or construction of light-rail systems in wide roadways, might be considered. But to be sustainable overall, a transportation system has to be flexible and adaptable and so must combine a mixture of modes.     

Influence of peak spreading and departure time choice on evaluation of its measures

Intelligent transport systems provide various means to improve capacity and travel time in road networks. Evaluation of the benefits of these improvements requires consideration of travellers' response to them. We consider a continuous‐time equilibrium model of departure time choice and identify a formula for the dynamic equilibrium departure rate profile. We develop the analysis to consider the effect on the cost incurred by travellers of ITS measures through their effects on each of the travel time in the absence of congestion, and the capacity for travel. This shows the importance in choice of departure time of travellers' values of time at each of the origin and destination of their journeys. We show the importance of these values of time in evaluation, and that if travellers value their time at both the origin and destination of their journeys, their responses will lead them to achieve a greater reduction in costs than would be achieved under free‐flow conditions.     

Effects of incorporating latent and attitudinal information in mode choice models

Abstract

Hybrid choice modelling approaches allow latent variables in mode choice utility functions to be addressed. However, defining attitude and behavior as latent variables is influenced by the researcher's assumptions. Therefore, it is better to capture the effects of latent behavioral and attitudinal factors as latent variables than defining behaviors and attitudes per se. This article uses a hybrid choice model for capturing such latent effects, which will herein be referred to as modal captivity effects in commuting mode choice. Latent modal captivity refers to the unobserved and apparently unexplained attraction towards a specific mode of transportation that is resulting from latent attitude and behavior of passengers in addition to the urban transportation system. In empirical models, the latent modal captivity variables are explained as functions of different observed variables. Empirical models show significant improvement in fitting observed data as well as improved understanding of travel behavior.