Large-Scale Application of a Combined Destination and Mode Choice Model Estimated with Mixed Stated and Revealed Preference Data

2017 ◽  
Vol 2669 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Michael Heilig ◽  
Nicolai Mallig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Revealed Preference ◽  
Demand Model ◽  
Agent Based ◽  
Preference Data ◽  
Mode Choice Model

The diffusion of new modes of transportation, such as carsharing and electric vehicles, makes it necessary to consider them along with traditional modes in travel demand modeling. However, there are two main challenges for transportation modelers. First, the new modes’ low share of usage leads to a lack of reliable revealed preference data for model estimation. Stated preference survey data are a promising and well-established approach to close this gap. Second, the state-of-the-art model approaches are sometimes stretched to their limits in large-scale applications. This research developed a combined destination and mode choice model to consider these new modes in the agent-based travel demand model mobiTopp. Mixed revealed and stated preference data were used, and new modes (carsharing, bikesharing, and electric bicycles) were added to the mode choice set. This paper presents both challenges of the modeling process, mainly caused by large-scale application, and the results of the new combined model, which are as good as those of the former sequential model although it also takes the new modes into consideration.

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.

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.

Analysis of Travel Time Saving Benefit by Understanding Individual Needs and Value of Activity Time: Case Study of Tokyo and Jakarta

2003 ◽  
Vol 1854 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Irwan Prasetyo ◽  
Daisuke Fukuda ◽  
Hirosato Yoshino ◽  
Tetsuo Yai
Keyword(s):  
Travel Time ◽  
Time Allocation ◽  
Choice Model ◽  
Stated Preference ◽  
Revealed Preference ◽  
Psychological Needs ◽  
Activity Time ◽  
Time Saving ◽  
Allocation Model ◽  
Preference Data

Quantification of the value of time (VOT) is important for measurement of the benefit of transportation projects in terms of travel time savings. In Japan, VOT is considered higher on weekends than on weekdays because on the weekend people have limited time to allocate to discretionary activities that are not normally done on weekdays, such as family care-related activities. In Indonesia, a culturally diverse country, providers and users seem to have different perceptions of VOT. A method of analyzing the value of activity time is presented. It argues that the benefit of travel time saving should be evaluated in more detail on weekends by considering the value of discretionary activities to explain these phenomena theoretically. Activity diary surveys were conducted in Tokyo, Japan, and Jakarta, Indonesia, to verify the influence of psychological needs on people's holiday activities. Finally, a time allocation model that uses the revealed preference data and a marginal activity choice model that uses stated preference data are proposed to calculate the value of activity time. The theories underpinning these models are Maslow's psychological needs, consumer theory in economics, and a discrete choice model. The empirical results show that an individual's priority of needs influences time allocation. In particular, the results show that in Tokyo, spending time with family on weekends is more valuable than other types of activities, while in Indonesia the value of spending time with family exceeds that of work time even on weekdays.

Understanding Issues in Airport Ground Access Modeling: Lessons from a Revealed Preference Study of New York Metropolitan Area Airports

2017 ◽  
Vol 2626 (1) ◽  
pp. 85-98
Author(s):  
Duncan Kisia
Keyword(s):  
New York ◽  
Choice Model ◽  
Mode Choice ◽  
Revealed Preference ◽  
Choice Models ◽  
Model Parameters ◽  
Preference Data ◽  
Access Mode ◽  
Analytical Strategies ◽  
Airport Access

Airport ground access mode choice models can provide a great deal of utility for airport facility managers tasked with landside access planning. However, the absence of definitive standards to guide the development of these airport planning tools often results in wide variations in methodological approaches that in turn generate counterintuitive mode choice model parameters and that often leads to improper understanding of the air passenger ground access trip. A new regional airport ground access model was developed in support of the New York City Department of Transportation’s LaGuardia Airport Access Alternatives Analysis Study. The air passenger model developed for the study included a set of market-segmented ground access mode choice models, developed by using revealed preference data from a 2005 survey commissioned by FAA. The model estimation process tested a number of analytical strategies to address some of the challenges typically encountered with revealed preference data and, in the process, uncovered some findings that should both aid future airport ground access mode choice modeling efforts and further illuminate the modeling community’s understanding of the value of time, particularly as it interacts with household income levels and various dimensions of business travel.

scholarly journals A meso-level empirical validation approach for agent-based computational economic models drawing on micro-data: a use case with a mobility mode-choice model

2021 ◽  
Vol 1 (6) ◽  
Author(s):  
Alperen Bektas ◽  
Valentino Piana ◽  
René Schuman
Keyword(s):  
Choice Model ◽  
Mode Choice ◽  
Statistical Distribution ◽  
Empirical Validation ◽  
Behavioral Patterns ◽  
Micro Data ◽  
Data Set ◽  
Agent Based ◽  
Meso Level ◽  
Mode Choice Model

AbstractThe complex nature of agent-based modeling may reveal more descriptive accuracy than analytical tractability. That leads to an additional layer of methodological issues regarding empirical validation, which is an ongoing challenge. This paper offers a replicable method to empirically validate agent-based models, a specific indicator of “goodness-of-validation” and its statistical distribution, leading to a statistical test in some way comparable to the p value. The method involves an unsupervised machine learning algorithm hinging on cluster analysis. It clusters the ex-post behavior of real and artificial individuals to create meso-level behavioral patterns. By comparing the balanced composition of real and artificial agents among clusters, it produces a validation score in [0, 1] which can be judged thanks to its statistical distribution. In synthesis, it is argued that an agent-based model can be initialized at the micro-level, calibrated at the macro-level, and validated at the meso-level with the same data set. As a case study, we build and use a mobility mode-choice model by configuring an agent-based simulation platform called BedDeM. We cluster the choice behavior of real and artificial individuals with the same ex-ante given characteristics. We analyze these clusters’ similarity to understand whether the model-generated data contain observationally equivalent behavioral patterns as the real data. The model is validated with a specific score of 0.27, which is better than about 95% of all possible scores that the indicator can produce. By drawing lessons from this example, we provide advice for researchers to validate their models if they have access to micro-data.

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

Sign in / Sign up

Export Citation Format

Share Document