Dynamic system‐optimal traffic assignment for a city using the continuum modeling approach

This paper presents a continuum dynamic traffic assignment model for a city in which the total cost of the traffic system is minimized: the travelers in the system are organized to choose the route to their destinations that minimizes the total cost of the system. Combined with the objective function, which defines the total cost and constraints such as certain physical and boundary conditions, a continuum model can be formulated as an optimization scheme with a feasible region in the function space. To obtain an admissible locally optimal solution to this problem, we first reformulate the optimization in discrete form and then introduce a heuristic method to solve it. This method converges rapidly with attractive computational cost. Numerical examples are used to demonstrate the effectiveness of the method. Copyright © 2013 John Wiley & Sons, Ltd.     

A differential evolution approach for the vehicle routing problem with backhauls and time windows

This paper presents a differential evolution algorithm (DEA) to solve a vehicle routing problem with backhauls and time windows (VRPBTW) and applied for a catering firm. VRPBTW is an extension of the vehicle routing problem, which includes capacity and time window constraints. In this problem, customers are divided into two subsets: linehaul and backhaul. Each vehicle starts from a depot and goods are delivered from the depot to the linehaul customers. Goods are subsequently brought back to the depot from the backhaul customers. The objective is to minimize the total distance that satisfies all of the constraints. The problem is formulated using mixed integer programming and solved using DEA. Proposed algorithm is tested with several benchmark problems to demonstrate effectiveness and efficiency of the algorithm and results show that our proposed algorithm can find superior solutions for most of the problems in comparison with the best known solutions. Hence, DEA was carried out for catering firm to minimize total transportation costs. Copyright © 2013 John Wiley & Sons, Ltd.     

A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling

The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.     

About attitudes and perceptions: finding the proper way to consider latent variables in discrete choice models

We provide an in-depth theoretical discussion about the differences between individual-specific latent constructs (representing attitudes, for example, but also other characteristics such as values or personality traits) and alternative-specific latent constructs (that may represent perceptions) affecting the choice-making process of individuals; we also carry out an empirical exercise to analyze their effects. This discussion is of importance, as the majority of papers considering attitudinal latent variables just take these as attributes affecting directly the utility of a certain alternative, while systematic taste variations are rarely considered and perceptions are mostly ignored. The results of our case study show that perceptions may indeed affect the decision making process and that they are able to capture a significant part of the variability that is normally explained by alternative specific constants. Furthermore, our results indicate that attitudes may be a reason for systematic taste variations, and that a proper categorization of latent variables, in accordance with underlying theory, may outperform the customary assumption of linearity.     

Discussing the “positive utilities” of autonomous vehicles: will travellers really use their time productively?

ABSTRACT

Autonomous vehicles (AVs) are expected to reshape travel behaviour and demand in part by enabling productive uses of travel time—a primary component of the “positive utility of travel” concept—thus reducing subjective values of travel time savings (VOT). Many studies from industry and academia have assumed significant increases in travel time use and reductions in VOT for AVs. In this position paper, I argue that AVs’ VOT impacts may be more modest than anticipated and derive from a different source. Vehicle designs and operations may limit activity engagement during travel, with AV users feeling more like car passengers than train riders. Furthermore, shared AVs may attenuate travel time use benefits, and productivity gains could be limited to long-distance trips. Although AV riders will likely have greater activity participation during travel, many in-vehicle activities today may be more about coping with commuting burdens than productively using travel time. Instead, VOT reductions may be more likely to arise from a different “positive utility”—subjective well-being improvements through reduced stresses of driving or the ability to relax and mentally transition. Given high uncertainty, further empirical research on the experiential, time use, and VOT impacts of AVs is needed.     

On incentives and optimal effort to improve bus transit performance

ABSTRACT

This paper studies incentive-based subsidy to transit systems to improve performance. It derives a formula for optimal effort that equalizes marginal cost and marginal benefit and derives some principles from it among which is that the larger the value of a transit system’s performance criterion the larger the effort it will exert to improve it. Next, using a constrained cost minimization approach it derives a nonlinear cost function that includes optimal effort and estimates it using an unbalanced panel data of single mode U.S. bus transit systems. The results show that optimal effort is the equivalent of seven full-time employees (15,243 labor hours) per year and in real terms it results in 0.6% cost saving (US$198,331) and US$836,796 in incentive subsidy per observation. The implications of these findings are examined.     

Examining the effects of out-of-home and in-home constraints on leisure activity participation in different seasons of the year

Using multi-day, multi-period travel diaries data of 56 days (four waves of two-week diaries) for 67 individuals in Stockholm, this study aims to examine the effects of out-of-home and in-home constraints (e.g. teleworking, studying at home, doing the laundry, cleaning and taking care of other household member[s]) on individuals’ day-to-day leisure activity participation decisions in four different seasons. This study also aims to explore the effects of various types of working schedules (fixed, shift, partial- and full-flexible) on individuals’ decisions to participate in day-to-day leisure activities. A pooled model (56 days) and wave-specific models (14 days in each wave) are estimated by using dynamic ordered Probit models. The effects of various types of working schedules are estimated by using 28 days of two waves’ data. The results show that an individual’s leisure activity participation decision is significantly influenced by out-of-home work durations but not influenced by in-home constraints, regardless of any seasons. Individuals with shift working hours engage less in day-to-day leisure activities than other workers’ types in both spring and summer seasons. The thermal indicator significantly affects individuals’ leisure activity participation decisions during the autumn season. Individuals exhibit routine behaviour characterized by repeated decisions in participating in day-to-day leisure activities that can last up to 14 days, regardless of any seasons.     

An analysis of bicycle route choice preferences in Texas,US

In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling). The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities, (4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics. The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs, red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle facility on the route.

Analysis of within-household effects and between-household differences in maintenance activity allocation

This paper investigates the allocation of household individuals to out-of-home maintenance activities using the rich activity-travel diary data from the San Francisco Bay Area. Two inter-related decisions are considered in this context: (i) whether the given activity episode is performed individually (solo) or jointly, and (ii) the person who participates in the activity, if it is a solo activity. To account for the conditional nature of the solo activity person selection, a nested mixed logit modeling framework is proposed and implemented to jointly analyze person allocation for all maintenance activities performed by a household on a given day. The model is used to investigate within-household effects and between-household differences. The proposed model relaxes some important restrictions in person allocation models by accounting for various sources of correlations and relaxing the assumption of constant variance across households. The proposed model is used to analyze the differences in person allocation between different types of households. The results indicate that life-cycle and household role, income, gender, employment status, and several types of constraints (activities including cost, time-availability, vehicle-availability, coordination constraints, and child-care obligations) affect person allocation decisions in the context of maintenance activities. The empirical results indicate the presence of various sources of correlations across persons, over activities, and within-household that are significant. In addition, the data also provides evidence that the unobserved variances in person selection utilities are not constant across households. A better understanding of these within-household interactions and between-household differences may be used in activity-based simulation models and to develop more effective and focused demand management measures.     

Assessing the employment agglomeration and social accessibility impacts of high speed rail in Eastern Australia

There is growing interest in establishing additional evidence, under the umbrella of the wider economy impacts of transport infrastructure projects, to support transport projects in general and public transport projects in particular that struggle to obtain benefit–cost ratios sufficient to gain the support of financial agencies. This paper focuses on one element of wider economy impacts, often referred to as effective economic (employment) density or employment agglomeration impacts, and another, less usually identified, social accessibility impact (SAI) which we refer to as effective social density, which in broad terms provide, correspondingly, evidence of the potential gains in work-related output (often referred to as productivity gains) and potential gains in non-work-related outputs. Both are associated with gains in individual and household benefit attributable to improved accessibility to services linked with populations and particular locations. The SAIs may capture some of the induced benefits in those jurisdictions where these are included routinely in benefit–cost analysis, and the methodology here is most appropriate to those settings where an existing calibrated demand curve may not be available. Using the proposed high speed rail (HSR) project between Sydney and Melbourne as the empirical setting, we identify economic agglomeration and social accessibility benefits for work and non-work related activity respectively. We find the former to be relatively small compared to the significant gains associated with non-work related travel activity, suggesting the greatest benefits associated with HSR, especially for those residents outside of the major metropolitan areas, will be non-work related travel activity.