Impact of Capacity, Crowding, and Vehicle Arrival Adherence on Public Transport Ridership: Los Angeles and Sydney Experience and Forecasting Approach

This paper describes innovative aspects in the development of regional travel models for both Sydney and Los Angeles. The overall approach was to incorporate the effects of capacity, crowding, and delayed vehicle arrivals in the network supply, mode choice, and assignment modules. Capacity and crowding modules were first developed and applied in Sydney. The Los Angeles effort has built upon that work and will also consider variations in vehicle arrivals. Most travel models ignore the fact that transit vehicles have limited capacity. The most behaviorally realistic way to implement this feature was through extra weight functions applied at the boarding station. A method was also developed to take into account crowding as a negative factor in the user perception of transit service quality. The work revealed that the probability of having a seat should be reflected in the segment in-vehicle time weight. There is a strong indication, from existing research and the Stated Preference surveys undertaken in Sydney, that in-vehicle time for a standing passenger should be weighted more onerously compared to a seating passenger. Ridership in heavily congested corridors in Los Angeles has been adversely impacted by delays in vehicle arrivals and severe bunching. Estimated wait and in-vehicle time functions will be incorporated in an integrated mode choice model and assignment procedures as part of the work reported in this paper. These methods can be used by modelers dealing with urban transport systems that have reached, or will reach, capacity and experience serious congestion related delays

Scenario-based approach to analysis of travel time reliability with traffic simulation models

This study established a conceptual framework for capturing the probabilistic nature of travel times with the use of existing traffic simulation models. The framework features three components: scenario manager, traffic simulation models, and trajectory processor. The scenario manager captures exogenous sources of variation in travel times through external scenarios consistent with real-world roadway disruptions. The traffic simulation models then produce individual vehicle trajectories for input scenarios while further introducing randomness that stems from endogenous sources of variation. Finally, the trajectory processor constructs distributions of travel time either for each scenario or for multiple scenarios to allow users to investigate scenario-specific impact on variability in travel times and overall system reliability. Within this framework, the paper discusses methodologies for performing scenario-based reliability analysis that focuses on (a) approaches to obtaining distributions of travel times from scenario-specific outputs and (b) issues and practices associated with designing and generating input scenarios. The proposed scenario-based approach was applied to a real-world network to show detailed procedures, analysis results, and their implications

Model for Person and Household Mobility Attributes

Individual mobility attributes have a strong impact on travel choices. In most applied models the only individual mobility attribute addressed is household car ownership. The new generation of activity-based models will be significantly expanded to include a wider range of mobility attributes, such as possession of a driver’s license, employer-provided transportation for commuting, car available from work or business, employer-provided or subsidized parking, and transit pass holding. The paper outlines a general framework for inclusion of individual mobility attributes in travel models and statistically explores two of them—household car ownership and person transit pass holding—on the basis of the London Area Travel Survey conducted in 2001. A joint choice model for both mobility attributes is estimated, and the corresponding behavioral insights are discussed. Among the key findings, a strong interdependence between mobility attributes such as car ownership, transit pass holding, and employer-provided transportation benefits can be mentioned. The paper also discusses the practical benefits of including mobility attributes in travel models for testing different policy scenarios

Advanced Activity-Based Models in Context of Planning Decisions

Travel demand modeling today is undergoing a transition from the conventional four-step models to a new generation of advanced activity-based models. The new generation of travel models is characterized by such distinctive features as the use of tours instead of trips as the base unit of travel, the generation of travel in the framework of daily activity agendas of individuals, and the use of fully disaggregate microsimulation techniques instead of the aggregate zonal calculations. Although the theoretical advantages of activity-based models—in particular, behavioral realism and consistency across all travel dimensions—are well known, the practical advantages in the context of planning decisions have rarely been discussed and documented. Experiences to date are summarized for application of activity-based models for various planning purposes in metropolitan regions of New York City; Columbus, Ohio; Atlanta, Georgia; San Francisco, California; and Montreal, Canada. The focus is on the practical planning questions and policies that were analyzed with these models and their relative strengths and advanced features compared with the four-step models. The planning questions and policies include congestion pricing schemes, high-occupancy-vehicle facilities, parking policy, testing impacts of demographic scenarios, and so on. It is shown that activity-based models are capable of treating these planning and policy issues at the level at which four-step models become inadequate

Air Passenger Preferences for Choice of Airport and Ground Access Mode in the New York City Metropolitan Region

In current practice, regional models are limited in their capability to analyze policies involving changes and improvements to airports (and their services) and ground access transportation. Typically, airports are treated only as employment centers or as special generators. Important and distinct features of air passenger travel affecting trip distribution and mode choice are rarely modeled explicitly. This paper presents the development of a joint airport and ground access mode choice model for the New York City metropolitan region based on an extensive survey of airport users. Unlike travel to and from most U.S. cities, air passengers flying to and from the New York region face a nontrivial choice of airports and ground access modes (including premium transit options). A nested logit model was formulated with airport choice at the upper level and ground access mode choice at the second level; however, a multinomial logit model was found to be statistically preferable. Results indicate that air passenger travel behavior is significantly different for business and nonbusiness travelers. Overall, willingness to pay for trips to and from the airport is much higher than for regular intracity trips. Average yield, access time, and access cost are the most important determinants of air passenger’s choice; demographics and trip characteristics are also significant. The developed tool was used for a comprehensive study of airport development alternatives in the New York region and is seen as the platform for additional data development and model extensions for future studies of air passenger service planning in the New York megaregion

Impact of individual daily travel pattern on value of time

A traveler’s willingness to pay for travel time savings depends on his/her socio-economic characteristics, travel purpose, and situational factors such as time pressure under which the travel is undertaken. Earlier literature on value of time (VOT) analysis focused mostly on the first two factors but did not examine the last factor thoroughly. However, in the real world we expect that (at least in most cases) a worker would be willing to pay more during the before-work period than during the after-work period since most of the workers should reach their respective work places by a certain time while the after-work schedule in general should be more relaxed. The additional time pressure during the before-work period makes time more valuable, thus increasing VOT. In some cases, where a worker with a flexible schedule has a high-priority post-work activity with a fixed schedule (for example, tickets to a concert) the situation can be reversed. The current study aims to capture such impacts of daily activity patterns on a person’s VOT using a comprehensive trip segmentation framework that is comprised of several integrated mode and trip departure time-of-day choice models. Each of these integrated models was estimated using both Revealed Preference and Stated Preference data from a large-scale GPS-assisted Household Travel Survey undertaken in Jerusalem, Israel. The results not only confirm the long-held hypothesis about variation of VOT by socio-economic factors and trip purpose but also shed light on the variation of VOT with daily travel patterns. To our knowledge, this is the first attempt to develop a rigorous modeling framework for capturing variation of VOT as a function of the individual daily activity pattern. An additional feature of the proposed approach is that it was practically implemented within the framework of an applied activity-based model

Scenario-Based Approach to Analysis of Travel Time Reliability with Traffic Simulation Models

This study established a conceptual framework for capturing the probabilistic nature of travel times with the use of existing traffic simulation models. The framework features three components: scenario manager, traffic simulation models, and trajectory processor. The scenario manager captures exogenous sources of variation in travel times through external scenarios consistent with real-world roadway disruptions. The traffic simulation models then produce individual vehicle trajectories for input scenarios while further introducing randomness that stems from endogenous sources of variation. Finally, the trajectory processor constructs distributions of travel time either for each scenario or for multiple scenarios to allow users to investigate scenario-specific impact on variability in travel times and overall system reliability. Within this framework, the paper discusses methodologies for performing scenario-based reliability analysis that focuses on (a) approaches to obtaining distributions of travel times from scenario-specific outputs and (b) issues and practices associated with designing and generating input scenarios. The proposed scenario-based approach was applied to a real-world network to show detailed procedures, analysis results, and their implications.This is a manuscript of an article published as Kim, Jiwon, Hani S. Mahmassani, Peter Vovsha, Yannis Stogios, and Jing Dong. "Scenario-based approach to analysis of travel time reliability with traffic simulation models." Transportation Research Record 2391, no. 1 (2013): 56-68. DOI: 10.3141%2F2391-06. Posted with permission.</p