Modeling and Analysis of Excess Commuting with Trip Chains
- URL: http://arxiv.org/abs/2008.11082v1
- Date: Tue, 25 Aug 2020 14:54:04 GMT
- Title: Modeling and Analysis of Excess Commuting with Trip Chains
- Authors: Yujie Hu, Xiaopeng Li
- Abstract summary: This research finds that traditional excess commuting studies underestimate both actual and optimal commute, while overestimate excess commuting.
Based on a case study of the Tampa Bay region of Florida, this research finds that traditional excess commuting studies underestimate both actual and optimal commute, while overestimate excess commuting.
- Score: 3.728629802579785
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Commuting, like other types of human travel, is complex in nature, such as
trip-chaining behavior involving making stops of multiple purposes between two
anchors. According to the 2001 National Household Travel Survey, about one half
of weekday U.S. workers made a stop during their commute. In excess commuting
studies that examine a region's overall commuting efficiency, commuting is,
however, simplified as nonstop travel from homes to jobs. This research fills
this gap by proposing a trip-chaining-based model to integrate trip-chaining
behavior into excess commuting. Based on a case study of the Tampa Bay region
of Florida, this research finds that traditional excess commuting studies
underestimate both actual and optimal commute, while overestimate excess
commuting. For chained commuting trips alone, for example, the mean minimum
commute time is increased by 70 percent from 5.48 minutes to 9.32 minutes after
trip-chaining is accounted for. The gaps are found to vary across trip-chaining
types by a disaggregate analysis by types of chain activities. Hence,
policymakers and planners are cautioned of omitting trip-chaining behavior in
making urban transportation and land use policies. In addition, the proposed
model can be adopted to study the efficiency of non-work travel.
Related papers
- Mining individual daily commuting patterns of dockless bike-sharing users: a two-layer framework integrating spatiotemporal flow clustering and rule-based decision trees [3.420408962606617]
This study presents a two-layer framework, integrating improved flow clustering methods and multiple rule-based decision trees.
The effectiveness and applicability of the framework is demonstrated by over 200 million dockless bike-sharing trip records in Shenzhen.
Lots of bike-sharing commuters live near urban villages and old communities with lower costs of living, especially in the central city.
arXiv Detail & Related papers (2024-07-13T09:30:51Z) - Wireless Crowd Detection for Smart Overtourism Mitigation [50.031356998422815]
This chapter describes a low-cost approach to monitoring overtourism based on mobile devices' wireless activity.
The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies.
They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database.
arXiv Detail & Related papers (2024-02-14T13:20:24Z) - Fair collaborative vehicle routing: A deep multi-agent reinforcement
learning approach [49.00137468773683]
Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other.
Traditional game theoretic solution concepts are expensive to calculate as the characteristic function scales exponentially with the number of agents.
We propose to model this problem as a coalitional bargaining game solved using deep multi-agent reinforcement learning.
arXiv Detail & Related papers (2023-10-26T15:42:29Z) - Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow [76.38515853201116]
Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving.
New autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories.
We present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response.
arXiv Detail & Related papers (2023-04-23T16:01:36Z) - Meta-Learning over Time for Destination Prediction Tasks [53.12827614887103]
A need to understand and predict vehicles' behavior underlies both public and private goals in the transportation domain.
Recent studies have found, at best, only marginal improvements in predictive performance from incorporating temporal information.
We propose an approach based on hypernetworks, in which a neural network learns to change its own weights in response to an input.
arXiv Detail & Related papers (2022-06-29T17:58:12Z) - Identifying Suitable Tasks for Inductive Transfer Through the Analysis
of Feature Attributions [78.55044112903148]
We use explainability techniques to predict whether task pairs will be complementary, through comparison of neural network activation between single-task models.
Our results show that, through this approach, it is possible to reduce training time by up to 83.5% at a cost of only 0.034 reduction in positive-class F1 on the TREC-IS 2020-A dataset.
arXiv Detail & Related papers (2022-02-02T15:51:07Z) - Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference
Approach [1.9111219197011353]
We show that the willingness of riders to request a shared ride has monotonically decreased from 27.0% to 12.8% throughout the year.
We find that the decline in sharing preference is due to an increased per-mile costs of shared trips and shifting shorter trips to solo.
arXiv Detail & Related papers (2022-01-30T01:17:36Z) - Impact of Autonomous Vehicle Technology on Long Distance Travel Behavior [0.0]
This study analyzed a travel survey to anticipate the impact of autonomous vehicles on long-distance trips.
Using AVs for pleasure trips can increase the number of travelers and stimulate people to choose longer distances.
For business trips, AV technology can reduce travel costs and job-related stress.
arXiv Detail & Related papers (2021-01-13T03:53:30Z) - Studying Person-Specific Pointing and Gaze Behavior for Multimodal
Referencing of Outside Objects from a Moving Vehicle [58.720142291102135]
Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing.
Existing outside-the-vehicle referencing methods focus on a static situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints.
We investigate the specific characteristics of each modality and the interaction between them when used in the task of referencing outside objects.
arXiv Detail & Related papers (2020-09-23T14:56:19Z) - The Benefits of Autonomous Vehicles for Community-Based Trip Sharing [20.51380943801894]
This work reconsiders the concept of community-based trip sharing proposed by Hasan et al.
It aims at quantifying the benefits of autonomous vehicles for community-based trip sharing, compared to a car-pooling platform where vehicles are driven by their owners.
The results of the optimization show that it can leverage autonomous vehicles to reduce the daily vehicle usage by 92%, improving upon the results of the original Commute Trip Sharing Problem by 34%, while also reducing daily vehicle miles traveled by approximately 30%.
arXiv Detail & Related papers (2020-08-28T18:12:13Z) - Decomposing Excess Commuting: A Monte Carlo Simulation Approach [1.14219428942199]
This research uses a Monte Carlo approach to simulate individual resident workers and individual jobs within census tracts.
It estimates commute distance and time from journey-to-work trips, and defines the optimal commute based on simulated individual locations.
arXiv Detail & Related papers (2020-05-30T14:09:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.