Dynamics of Activity Time-of-Day Choice

2017 ◽  
Vol 2665 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Ramin Shabanpour ◽  
Nima Golshani ◽  
Joshua Auld ◽  
Abolfazl (Kouros) Mohammadian
Keyword(s):  
Travel Time ◽  
Decision Process ◽  
Travel Demand ◽  
Choice Model ◽  
Time Of Day ◽  
Demand Model ◽  
Start Time ◽  
Time Duration ◽  
Time Horizons ◽  
Activity Attributes

This study explored travelers’ decision behavior in selecting activity start times. The study examined the problem in the context of the Agent-based Dynamic Activity Planning and Travel Simulation (ADAPTS) activity-based travel demand model for the Chicago, Illinois, metropolitan area. A unique feature of the ADAPTS framework is its consideration of planning horizons for various activity attributes. Naturally, the various attributes of an activity—such as start time, duration, location, party involvement, and mode of travel—can be planned in different time horizons. An attribute that is planned affects the choice of other activity attributes. Therefore, developing a true behavioral time-of-day choice model would not be possible unless the planning order of activity attributes and the dynamics of travelers’ decision-making processes are taken into account. Similarly, it can be argued that there should be fundamental differences in the time-of-day decision process when other attributes of the activity are not yet planned but are to be decided at a later time. The presented time-of-day model aims to capture the dynamics of this decision process by considering the planning time horizons of other attributes of the activity, as well as the outcomes of the decisions. The study adopted the discrete choice approach to model activity timing decisions and a hybrid utility maximization and developed a regret minimization model to account for the heterogeneity of decision rules across choice variables. Analysis of the estimation results and parameter elasticities indicates that higher expected travel time, variations in travel time, and schedule occupancy rates for different time choices can significantly increase the regret value of the corresponding choice and therefore affect the time-of-day choice.

scholarly journals What Matters Most in Transportation Demand Model Specifications: A Comparison of Outputs in a Mid-size Network

2015 ◽  
Author(s):  
T. Donna Chen ◽  
Kara Kockelman ◽  
Yong Zhao
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Mode Choice ◽  
Demographic Data ◽  
Time Of Day ◽  
Demand Model ◽  
Land Use Patterns ◽  
Transportation Demand ◽  
Step Model ◽  
The Impact

This paper examines the impact of travel demand modeling (TDM) disaggregation techniques in the context of medium-sized communities. Specific TDM improvement strategies are evaluated for predictive power and flexibility with case studies based on the Tyler, Texas, network. Results suggest that adding time-of-day disaggregation, particularly in conjunction with multi-class assignment, to a basic TDM framework has the most significant impacts on outputs. Other strategies shown to impact outputs include adding a logit mode choice model and incorporating a congestion feedback loop. For resource-constrained communities, these results show how model output and flexibility vary for different settings and scenarios.BACKGROUND Transportation directly provides for the mobility of people and goods, while influencing land use patterns and economic activity, which in turn affect air quality, social equity, and investment decisions. Driven by the need to forecast future transportation demand and system performance, Manheim (1979) and Florian et al. (1988) introduced a transportation analysis framework for traffic forecasting using aggregated data that provide the basis for what is known as the four-step model: a process involving trip generation, then trip distribution and mode choice, followed by route choice. Aggregating demographic data at the zone level, the four-step model generates trip productions based on socioeconomic data (e.g., household counts by income and size) and trip attractions primarily based on jobs counts. The model then proportionally distributes trips between each origin and destination (OD) zone pair based on competing travel attractions and impedances, under the assumption that OD pairings with higher travel costs draw fewer trips. Trips between each OD pair are split among a variety of transportation modes, allocating trips to private vehicle, transit, or other

scholarly journals Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

Regional Bicycle Network Evaluation and Strategic Planning: A Quantitative Methodological Approach Despite Limited Data Sources for Cycling

2021 ◽  
pp. 036119812110288
Author(s):  
Alex van Dulmen ◽  
Martin Fellendorf
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Methodological Approach ◽  
Data Availability ◽  
Data Sources ◽  
Test Case ◽  
Demand Model ◽  
Limited Data ◽  
Demand Models ◽  
Bicycle Infrastructure

In cases where budgets and space are limited, the realization of new bicycle infrastructure is often hard, as an evaluation of the existing network or the benefits of new investments is rarely possible. Travel demand models can offer a tool to support decision makers, but because of limited data availability for cycling, the validity of the demand estimation and trip assignment are often questionable. This paper presents a quantitative method to evaluate a bicycle network and plan strategic improvements, despite limited data sources for cycling. The proposed method is based on a multimodal aggregate travel demand model. Instead of evaluating the effects of network improvements on the modal split as well as link and flow volumes, this method works the other way around. A desired modal share for cycling is set, and the resulting link and flow volumes are the basis for a hypothetical bicycle network that is able to satisfy this demand. The current bicycle network is compared with the hypothetical network, resulting in preferable actions and a ranking based on the importance and potentials to improve the modal share for cycling. Necessary accompanying measures for other transport modes can also be derived using this method. For example, our test case, a city in Austria with 300,000 inhabitants, showed that a shift of short trips in the inner city toward cycling would, without countermeasures, provide capacity for new longer car trips. The proposed method can be applied to existing travel models that already contain a mode choice model.

scholarly journals Disparities in travel times between car and transit: Spatiotemporal patterns in cities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuan Liao ◽  
Jorge Gil ◽  
Rafael H. M. Pereira ◽  
Sonia Yeh ◽  
Vilhelm Verendel
Keyword(s):  
Travel Time ◽  
Travel Demand ◽  
Time Of Day ◽  
Sao Paulo ◽  
São Paulo ◽  
Real World Data ◽  
Car Use ◽  
Real Time Traffic ◽  
Average Travel Time ◽  
Car Travel

AbstractCities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio $$R$$R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: $$R < 1$$R<1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.

scholarly journals Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Rolf Moeckel ◽  
Nico Kuehnel ◽  
Carlos Llorca ◽  
Ana Tsui Moreno ◽  
Hema Rayaprolu
Keyword(s):  
Travel Demand ◽  
Time Budget ◽  
Time Of Day ◽  
Destination Choice ◽  
Trip Generation ◽  
Demand Model ◽  
Agent Based ◽  
Travel Mode Choice ◽  
Travel Time Budget ◽  
Aggregate Nature

The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.

Travel Demand Modeling and Conformity Determination: Atlanta Regional Commission Case Study

2002 ◽  
Vol 1817 (1) ◽  
pp. 172-176 ◽  
Author(s):  
Guy Rousseau ◽  
Tracy Clymer
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
Mode Choice ◽  
Time Of Day ◽  
Traffic Model ◽  
Demand Model ◽  
Primary Input ◽  
Land Use Model ◽  
Home Based ◽  
Travel Demand Model

The Atlanta Regional Commission (ARC) regional travel demand model is described as it relates to its link-based emissions postprocessor. In addition to conformity determination, an overview of other elements is given. The transit networks include the walk and highway access links. Trip generation addresses trip production, trip attraction, reconciliation of productions and attractions, and special adjustments made for Hartsfield Atlanta International Airport. Trip distribution includes the application of the composite impedance variable. In the mode choice model, home-based work uses a logit function, whereas nonwork uses information from the home-based work to estimate modal shares. Traffic assignment includes preparation of time-of-day assignments. The model assigns single-occupancy vehicles, high-occupancy vehicles, and trucks by using separate trip tables. The procedures can accept or prohibit each of the three types of vehicles from each highway lane. Feedback between the land use model and the traffic model is accounted for via composite impedances generated by the traffic model and is a primary input to the land use model DRAM/EMPAL. The land use model is based on census tract geography, whereas the travel demand model is based on traffic analysis zones that are subareas within census tracts. The ARC model has extended the state of the practice by using the log sum variable from mode choice as the impedance measure rather than the standard highway time. This change means that the model is sensitive not only to highway travel time but also to highway and transit costs.

An Integrated, Validated, and Applied Activity-Based Dynamic Traffic Assignment Model for the Baltimore-Washington Region

2018 ◽  
Vol 2672 (51) ◽  
pp. 45-55 ◽  
Author(s):  
Lei Zhang ◽  
Di Yang ◽  
Sepehr Ghader ◽  
Carlos Carrion ◽  
Chenfeng Xiong ◽  
...  
Keyword(s):  
Daily Activity ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Integrated Model ◽  
Time Of Day ◽  
Practical Implementation ◽  
Baseline Scenario ◽  
Demand Model ◽  
Dynamic Traffic

The paper discusses the integration process and initial applications of a new model for the Baltimore-Washington region that integrates an activity-based travel demand model (ABM) with a dynamic traffic assignment (DTA) model. Specifically, the integrated model includes InSITE, an ABM developed for the Baltimore Metropolitan Council, and DTALite, a mesoscopic DTA model. The integrated model simulates the complete daily activity choices of individuals residing in the model region, including long-term choices, such as workplace location; daily activity patterns, including joint household activities and school escorting; activity location choices; time-of-day choices; mode choices; and route choices. The paper describes the model development and integration approach, including modeling challenges, such as the need to maintain consistency between the ABM and DTA models in terms of temporal and spatial resolution, and practical implementation issues, such as managing model run time and ensuring sufficient convergence of the model. The integrated model results have been validated against observed daily traffic volumes and vehicle-miles traveled (VMT) for various functional classes. A land-use change scenario that analyzes the redevelopment of the Port Covington area in Baltimore is applied and compared with the baseline scenario. The validation and application results suggest that the integrated model outperforms a static assignment-based ABM and could capture behavioral changes at much finer time resolutions.

scholarly journals Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China

2020 ◽  
Vol 12 (2) ◽  
pp. 620 ◽  
Author(s):  
Qian Duan ◽  
Xin Ye ◽  
Jian Li ◽  
Ke Wang
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Stated Preference ◽  
Influential Factors ◽  
Environmental Benefits ◽  
Empirical Modeling ◽  
Unit Price ◽  
Marginal Effect ◽  
Demand Model ◽  
Potential Demand

Carsharing is an emerging commute mode in China, which may produce social and environmental benefits. This paper aims to develop a commute mode choice model to explore influential factors and quantify their impacts on the potential demand for carsharing in Shanghai. The sample data were obtained from a revealed preference (RP) and stated preference (SP) survey and integrated with level-of-service attributes from road and transit networks. The RP survey collected commuters’ trip information and socioeconomic and demographic characteristics. In the SP survey, four hypothetical scenarios were designed based on carsharing’s unit price to collect commuters’ willingness to shift to carsharing. Data fusion method was applied to fuse RP and SP models. The joint model identified the target group of choosing carsharing with certain socioeconomic and demographic attributes, such as gender, age, income, household member, household vehicle ownership, and so on. It also indicates that the value of time (VOT) for carsharing is 35.56 RMB Yuan (5.08 US Dollar)/h. The elasticity and marginal effect analysis show that the direct elasticity of carsharing’s fare on its potential demand is −0.660, while the commuters, who have a more urgent plan on car purchase or are more familiar with the carsharing service, have much higher probabilities to choose carsharing as their commute modes. The developed model is expected to be applied to the urban travel demand model, providing references for the formulation of carsharing operation scheme and government policy.

On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit

1977 ◽  
Vol 9 (3) ◽  
pp. 285-344 ◽  
Author(s):  
H C W L Williams
Keyword(s):  
Economic Evaluation ◽  
Travel Demand ◽  
Choice Model ◽  
Demand Model ◽  
Random Utility Theory ◽  
Underlying Assumption ◽  
Evaluation Measures ◽  
Planning Models ◽  
Demand Models ◽  
Special Cases

This paper examines a variety of issues within the context of two main themes: the formation of travel demand models and economic evaluation measures which are mutually consistent within a theory of rational choice; and a consideration of the structure of models which are representations of the trip decision process over several dimensions: location, mode, and route. Random utility theory is invoked to explore both the role and properties of composite costs or index prices in the ‘recursive’ approach to the structuring of travel choice models, and their significance in the economic evaluation problem. It is shown that the specification of these costs must be made very precisely, with respect to the demand model form chosen, in order to retain the underlying assumption that the traveller is an optimal decisionmaker. It is argued that the structure of ‘simultaneous’ models currently in use is inconsistent with the form of utility function assumed to generate those models. Furthermore, it is shown that the ‘simultaneous’ and ‘recursive’ forms are special cases of a more general choice model structure which takes specific account of correlation or ‘commonality’ of trip attributes. A number of applications are discussed in which consistent demand models and perceived user benefit measures are constructed. These include the formation of strategic transport planning models and of models for mixed-mode, multimode, and multiroute systems. The formalism allows definitive answers to be given to a number of problems of current interest in transportation planning, which have been incorrectly or incompletely treated.

Modeling Emergency Managers’ Hurricane Evacuation Decisions

2017 ◽  
Vol 2604 (1) ◽  
pp. 82-87 ◽  
Author(s):  
Ravindra Gudishala ◽  
Chester Wilmot
Keyword(s):  
Discrete Choice ◽  
Research Study ◽  
Choice Model ◽  
Independent Predictor ◽  
Time Of Day ◽  
Clearance Time ◽  
Demand Model ◽  
Hurricane Evacuation ◽  
Emergency Managers ◽  
Emergency Manager

Emergency management and decision support system (EMDSS) tools play an important role in assisting emergency managers with making important decisions about the movement of people to safety when a jurisdiction is threatened by a storm. One of the important components of an EMDSS is an evacuation demand model that predicts whether and when households will evacuate when they are threatened by a storm. A critical input to that model is an emergency manager's decision to issue an evacuation notice. No existing mathematical models predict whether and when an emergency manager will issue an evacuation notice on the basis of a hurricane forecast and other contextual factors. To fill this gap, this research study sought to develop a model that would predict if and when an emergency manager would issue an evacuation notice when a jurisdiction was threatened by a storm. Data from poststorm assessment surveys and newspaper archives were used to retrieve past decisions made by evacuation managers for five storms in 45 coastal counties or parishes. The data were then used to develop a discrete choice model by use of the time-dependent sequential logit paradigm. Five independent predictor variables—storm surge, clearance time, time to landfall, hurricane category, and time of day—were found to be good predictors of the decisions made by emergency managers. This model could be useful to emergency managers to estimate how other emergency managers decide to evacuate an area when they are faced with an evacuation decision. The model could also benefit researchers and practitioners engaged in modeling and understanding hurricane evacuation behavior.

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