Temporal-Spatial Microassignment and Sequencing of Travel Demand with Activity-Trip Chains

2003 ◽  
Vol 1831 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Ahmed F. Abdelghany ◽  
Hani S. Mahmassani
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Travel Demand ◽  
User Equilibrium ◽  
Stochastic Dynamic ◽  
Arrival Times ◽  
Schedule Delay ◽  
Trip Chain ◽  
Trip Chaining ◽  
Distance Travel

A stochastic temporal–spatial microassignment and activity sequencing model for activity–trip chains is presented. In this model, trip-chain patterns are defined by the respective locations of destinations in the chain, preferred arrival times at these destinations, and the activity durations at the intermediate destinations; they are given as input to the model. A stochastic dynamic user equilibrium problem is formulated and solved for this purpose. In this problem, drivers simultaneously seek to determine their departure time, route choice, and sequence of their intermediate activities at the origin to minimize their perceived travel cost. This perceived cost is typically a function of the travel time and the schedule delay at the intermediate and final destinations. The model is presented through a study of the relative efficiency of carpooling and trip-chaining travel behavior in a network context. In that example, the performance of travelers who have the option to carpool and chain trips is compared with that of households with single-occupant and individual trip-based travel. Several measures of travel performance, including travel distance, travel time, and schedule delay, are considered for that comparison.

Understand the Multi-Level Effects of the Built Environment on Trip-Chaining Behavior

2019 ◽  
Vol 2673 (4) ◽  
pp. 640-651
Author(s):  
Hao Pang ◽  
Ming Zhang
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Linear Models ◽  
Interactive Effect ◽  
Puget Sound ◽  
Residential Density ◽  
Vehicle Ownership ◽  
Trip Chain ◽  
Trip Chaining ◽  
Relationship Of

The debate on the effects of the built environment (BE) on travel behavior has been ongoing despite a large number of studies completed in the past three decades. This study aims to inform the debate by extending the BE–travel behavior investigation to the scope of trip-chaining. Specifically, the study conceptualized the contexture frame for the relationship of BE attributes and trip-chain travel behavior and estimated 2-level hierarchical linear models (HLM) of chained trip tours with travel survey data from the Puget Sound region. The results show that travelers who live in areas with better transit access, higher residential and non-residential density, and higher level of land use mixture generated low percentage of miles traveled by vehicle (PVMT) during their daily tours. Furthermore, considering the cross-level interactive effect, the study demonstrates that the impacts of the non-residential density at work location and the residential density at home location on PVMT are moderated by vehicle ownership.

scholarly journals Analysis of Time of Day Fare Discounts on Urban Mass Transit Travel Behavior, Crowding, and Waiting Time

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xiao Guo ◽  
Huijun Sun
Keyword(s):  
Objective Function ◽  
Waiting Time ◽  
Travel Behavior ◽  
Travel Demand ◽  
Mass Transit ◽  
Time Of Day ◽  
Pressure Equalization ◽  
Schedule Delay ◽  
Urban Mass Transit ◽  
Artificial Network

Every morning, commuters select the regularly dispatched urban mass transit for traveling from a residential area to a workplace. This paper aims to find an optimal discount fare and time intervals on morning peak hour. As a direct and flexible traffic economic instrument, fares can influence commuters’ behavior. Therefore, fare discount has been proposed to regulate traffic flow in different time. Two models have been analyzed to describe it with schedule delay because of the travel demand size. The first objective function is constructed on pressure equalization when the travel demand is small. The other objective function is to minimize total waiting time when the travel demand is large. In the end, numerical examples based on an artificial network are performed to characterize fare discount models.

Stochastic User Equilibrium Model with Implicit Travel Time Budget Constraint

2008 ◽  
Vol 2085 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Tony E. Smith ◽  
Chao-Che Hsu ◽  
Yueh-Ling Hsu
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Time Budget ◽  
User Equilibrium ◽  
Time Constraints ◽  
Stochastic User Equilibrium ◽  
Trade Offs ◽  
Travel Time Budget ◽  
Travel Decisions ◽  
The Impact

Although time constraints on travel behavior have been widely recognized, little effort has been made to incorporate such constraints into the traditional stochastic user equilibrium (SUE) framework. The major objective of this research is to fill this gap by incorporating travel time constraints into the SUE model by means of a nonlinear perceived travel time function. This modified model, designated the travel time budget model, focuses primarily on discretionary travel behavior (such as shopping trips) and hence also allows the possibility of deferring travel decisions by incorporating an additional choice alternative designated the shop-less-frequently alternative. This model is compared with the traditional SUE model by using a simulated travel scenario on a test network designed to reflect a practical planning situation. The simulation shows that when attractiveness levels are increased by the introduction of a new shopping opportunity, the presence of travel time constraints can lead to significantly smaller predicted travel volumes than those of the traditional SUE model. More important, it shows that the overall pattern of travel can be quite different. In particular, travel to the shopping destination with enhanced attractiveness can actually decrease for some origin locations. The findings suggest that when an attempt is made to evaluate the impact of planning alternatives on future traffic patterns, it is vital to consider not only the cost of time itself but also the time trade-offs between travel and other human activities.

scholarly journals A Transportation Network Paradox: Consideration of Travel Time and Health Damage due to Pollution

2020 ◽  
Vol 12 (19) ◽  
pp. 8107
Author(s):  
Zhaolin Cheng ◽  
Laijun Zhao ◽  
Huiyong Li
Keyword(s):  
Air Pollution ◽  
Travel Time ◽  
Travel Demand ◽  
Transportation Network ◽  
Flow Distribution ◽  
System Optimization ◽  
User Equilibrium ◽  
Braess Paradox ◽  
Health Damage ◽  
Route Choices

In cities with serious air pollution, travel time and health damage significantly affect route choice by travelers (e.g., motorcycle and scooter drivers). Consequently, the classical Braess paradox is no longer realistic because it only considers the traveler’s value of time (VOT). In the current study, we describe a new transportation network paradox that considers both the VOT and the traveler’s perception of pollution damage. To examine the conditions that create the new paradox, we developed a novel method to compute a total comprehensive cost that combines the VOT with health damage. We analyzed the conditions for the new paradox and the system’s performance using a user equilibrium model and system optimization. Furthermore, an improved model is used to analyze how different transport modes influence the Braess paradox. We found that whether the new paradox occurs and the potential improvement of the system’s performance depend on whether the total travel demand falls within critical ranges. The bounds of these ranges depend on the values of the parameters in the function that describes the health damage and the link travel time function. In addition, high health damage significantly affects route choices and traffic flow distribution. This paper presents a new perspective for decision-making by transportation planners and for route choices in cities with serious air pollution.

Incorporating Travel Time Reliability in Equitable Congestion Pricing Schemes for Heterogeneous Users and Bimodal Networks

2021 ◽  
pp. 036119812110197
Author(s):  
Fatemeh Fakhrmoosavi ◽  
Ali Zockaie ◽  
Khaled Abdelghany
Keyword(s):  
Travel Time ◽  
Optimization Algorithm ◽  
Travel Demand ◽  
Demand Management ◽  
Congestion Pricing ◽  
User Equilibrium ◽  
Travel Time Reliability ◽  
Travel Demand Management ◽  
Equity Issues ◽  
Total Travel Time

Congestion pricing is proposed as an effective travel demand management strategy to circumvent the problem of congestion and generate revenue to finance developmental projects. There are several studies focusing on optimal pricing strategies to minimize the congestion level or maximize the revenue of the system. However, with regard to equity issues, benefiting only users with higher value of time is claimed to be the main factor that prevents implementation of such policies in practice. While many studies aimed to tackle the equity issues by certain welfare analyses, most of these studies fail to fully consider realistic features of users’ behavior and the uncertainty in link travel times. Given the variability of travel time in real-world networks and the impacts of pricing policies on path travel time distributions, it is important to consider the users’ reliability valuations, in addition to their travel time valuations. Thus, the goal in this study is to find an equitable pricing scheme that minimizes the total travel time of auto users in a general bimodal network considering heterogeneous users with different values of time and reliability. A particle swarm optimization algorithm is proposed to find self-funded and Pareto-improving optimal toll values. A reliability-based user equilibrium algorithm is embedded into this optimization algorithm to assign travelers to the equilibrated paths for different user classes given toll values. The proposed approach is successfully applied to a modified Sioux Falls network to explore impacts of subsidization, congestion level, and considering travel time reliability on the pricing strategy and its effectiveness.

Trip-Chaining Trends in the United States

2005 ◽  
Vol 1917 (1) ◽  
pp. 199-204 ◽  
Author(s):  
Nancy McGuckin ◽  
Johanna Zmud ◽  
Yukiko Nakamoto
Keyword(s):  
United States ◽  
Travel Demand ◽  
Model Development ◽  
The United States ◽  
Policy Implications ◽  
Demand Forecast ◽  
Travel Demand Management ◽  
Trip Chain ◽  
Trip Chaining ◽  
Work Trip

This paper uses data from the 1995 Nationwide Personal Transportation Survey and the 2001 National Household Travel Survey to examine trip-chaining trends in the United States. The research focuses on trip chaining related to the work trip and contrasts travel characteristics of workers who trip chain with those who do not, including their distance from work, current levels of trip making, and the purposes of stops made within chains. Trends examined include changes in the purpose of stops and in trip-chaining behavior by gender and life cycle. A robust growth in trip chaining occurred between 1995 and 2001, nearly all in the direction of home to work. Men increased their trip chaining more than women, and a large part of the increase was to stop for coffee (the Starbucks effect). It was found that workers who trip chain live farther from their workplaces than workers who do not. It was also found that, in two-parent, two-worker households that drop off children at school, women are far more likely than men to incorporate that trip into their commute and that those trips are highly constrained between 8:00 a.m. and 9:00 a.m. An analysis was done of workers who stopped to shop and those who did not but made a separate shopping trip from home; a large potential to increase trip-chaining behavior in shopping trips was found. Results of these analyses have important policy implications as well as implications for travel demand forecast model development. Finally, this paper uses these analyses to develop conclusions about the utility of transportation policies and programs that use the promotion of trip chaining as a primary travel demand management strategy.

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

Linking Residential Parking to Automobile Transportation Impact Outcomes at a Development Level

2020 ◽  
pp. 036119812096479
Author(s):  
Kristina M. Currans ◽  
Gabriella Abou-Zeid ◽  
Nicole Iroz-Elardo
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Management ◽  
Management Strategies ◽  
Land Development ◽  
Future Research ◽  
Alternative Mode ◽  
Travel Demand Management ◽  
Development Level ◽  
Automobile Demand

Although there exists a well-studied relationship between parking policies and automobile demand, conventional practices evaluating the transportation impacts of new land development tend to ignore this. In this paper, we: (a) explore literature linking parking policies and vehicle use (including vehicle trip generation, vehicle miles traveled [VMT], and trip length) through the lens of development-level evaluations (e.g., transportation impact analyses [TIA]); (b) develop a conceptual map linking development-level parking characteristics and vehicle use outcomes based on previously supported theory and frameworks; and (c) evaluate and discuss the conventional approach to identify the steps needed to operationalize this link, specifically for residential development. Our findings indicate a significant and noteworthy dearth of studies incorporating parking constraints into travel behavior studies—including, but not limited to: parking supply, costs or pricing, and travel demand management strategies such as the impacts of (un)bundled parking in housing costs. Disregarding parking in TIAs ignores a significant indicator in automobile use. Further, unconstrained parking may encourage increases in car ownership, vehicle trips, and VMT in areas with robust alternative-mode networks and accessibility, thus creating greater demand for vehicle travel than would otherwise occur. The conceptual map offers a means for operationalizing the links between: the built environment; socio-economic and demographic characteristics; fixed and variable travel costs; and vehicle use. Implications for practice and future research are explored.

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