Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

Journal Browser

Modeling Activity-Travel Behavior for Sustainable Transportation

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 20541

Share This Special Issue

Special Issue Editors

Dr. Dongfang Ma

E-Mail Website
Guest Editor

Institute of Intelligent Transportation System, Zhejiang University, Hangzhou 310058, China
Interests: maritime data mining; intelligent control theory and method; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Dr. Sheng Jin

E-Mail Website
Guest Editor

Institute of Intelligent Transportation System, Zhejiang University, Hangzhou 310058, China
Interests: traffic flow theory; intelligent transportation systems; traffic flow data mining

Prof. Dr. Dianhai Wang

E-Mail Website
Guest Editor

Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China
Interests: traffic control optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We face complicated and difficult issues in urban traffic networks, such as traffic congestion, parking problems, and vehicle emissions. Recently, many solutions for sustainable urban transportation using congestion charging, free transfer of public transport, intelligent transportation systems, artificial intelligence, and big data have been proposed for tackling these problems, and have been implemented in a number of cities. All of the solutions and other activities (e.g., large-scale competitions and road maintenance construction) will lead some travelers to change their behaviors in various ways, including travel mode, route, and departure time. These changes will then bring new traffic flow patterns in terms of total demand, peak hours, and distribution of travel demand. Thus, the aforementioned solutions and activities should be optimized with the information on activity-travel behavior.

This Special Issue will highlight new opportunities and challenges for modeling travel behavior with various activities, which will promote sustainable urban traffic networks. We welcome papers on:

Modelling travel behavior and evaluating the influence of various management policies and some activities, including congestion charging, parking reservation, dynamic parking fees, large-scale competitions, and road maintenance construction, focusing on the integration of traffic flow data with travel behavior choice.
New technologies and methods for mobility as a service (MaaS) and sustainable urban transportation, including multi-mode combined path planning, smart parking, integrated booking, and travel incentives.
New measures for travel cost calculation, and new technologies to mine the evolution regularity of multi-modal traffic flow patterns and calculate the transfer rate among different travel modes.
Case studies of traffic planning and management schemes based on activity-travel behavior models, including policies, regulations, and measures.
Modeling the portraits of individual travelers based on big data.

Dr. Dongfang Ma
Dr. Sheng Jin
Prof. Dr. Dianhai Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

activity-travel behavior
mobility as a service
traffic flow pattern mining
travel behavior
travel incentives

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

13 pages, 2421 KiB

Open AccessArticle

The Morning Commute Problem with Ridesharing When Meet Stochastic Bottleneck

by Zipeng Zhang and Ning Zhang

Sustainability 2021, 13(11), 6040; https://doi.org/10.3390/su13116040 - 27 May 2021

Cited by 4 | Viewed by 1498

Abstract

This paper extends Vickrey’s point-queue model to study ridesharing behavior during a morning commute with uncertain bottleneck location. Unlike other ridesharing cost analysis models, there are two congestion cases and four dynamic departure patterns in our model: pre-pickup congestion case and post-pickup congestion case; both early pattern, both late pattern, late for pickup but early for work pattern, and early for pickup but late for work pattern. Analytical results indicate that the dynamic property of the mixed commuters equilibrium varies with the endogenous penetration rates associated with ridesharing commutes, as well as the schedule difference between pickup and work. This work is expected to promote the development of ridesharing to mitigate the traffic congestion and motivate related research of schedule coordination for regulating the ridesharing travel behavior in terms of the morning commute problem. Full article

(This article belongs to the Special Issue Modeling Activity-Travel Behavior for Sustainable Transportation)

► Show Figures

Figure 1

20 pages, 1125 KiB

Open AccessArticle

Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

by Wei Qin, Linhong Wang, Yuhan Liu and Cheng Xu

Sustainability 2021, 13(9), 4689; https://doi.org/10.3390/su13094689 - 22 Apr 2021

Cited by 12 | Viewed by 1888

Abstract

Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model. Full article

(This article belongs to the Special Issue Modeling Activity-Travel Behavior for Sustainable Transportation)

► Show Figures

Figure 1

15 pages, 12713 KiB

Open AccessArticle

Safety-Critical Event Identification on Mountain Roads for Traffic Safety and Environmental Protection Using Support Vector Machine with Information Entropy

by Zihao Wen, Hui Zhang and Ronghui Zhang

Sustainability 2021, 13(8), 4426; https://doi.org/10.3390/su13084426 - 15 Apr 2021

Cited by 9 | Viewed by 1714

Abstract

Traffic accidents, which cause loss of life and pollution, are a social concern. The complex traffic environment on mountain roads increases the harm caused by traffic accidents. This study aimed to identify safety-critical events related to accidents on mountain roads to understand the causes of the accidents, improve traffic safety, and protect the environment. In this study, a naturalistic-driving data collection system, consisting of approximately 8000 km of naturalistic-driving data from 20 drivers driving on mountain roads, was developed. Using these data, a comparative analysis of the identification performance of the support vector machine (SVM), backpropagation neural network (BPNN), and convolutional neural network (CNN) methods was conducted. The SVM was found to yield optimal performance. To improve the identification performance, the yaw rate and information entropy of the data were added as input variables. The improved SVM method yielded an identification accuracy of 90.64%, which was approximately 15% higher than that yielded by the traditional SVM. Moreover, the false positive and false negative rates of the improved SVM were reduced by approximately 10% and 20%, respectively, compared with the traditional SVM. The results demonstrated that the improved SVM method can identify safety-critical events on mountain roads accurately and efficiently. Full article

(This article belongs to the Special Issue Modeling Activity-Travel Behavior for Sustainable Transportation)

► Show Figures

Figure 1

18 pages, 3234 KiB

Open AccessArticle

Linking Mode Choice with Travel Behavior by Using Logit Model Based on Utility Function

by Wissam Qassim Al-Salih and Domokos Esztergár-Kiss

Sustainability 2021, 13(8), 4332; https://doi.org/10.3390/su13084332 - 13 Apr 2021

Cited by 29 | Viewed by 4276

Abstract

The currently available transport modeling tools are used to evaluate the effects of behavior change. The aim of this study is to analyze the interaction between the transport mode choice and travel behavior of an individual—more specifically, to identify which of the variables has the greatest effect on mode choice. This is realized by using a multinomial logit model (MNL) and a nested logit model (NL) based on a utility function. The utility function contains activity characteristics, trip characteristics including travel cost, travel time, the distance between activity place, and the individual characteristics to calculate the maximum utility of the mode choice. The variables in the proposed model are tested by using real observations in Budapest, Hungary as a case study. When analyzing the results, it was found that “Trip distance” variable was the most significant, followed by “Travel time” and “Activity purpose”. These parameters have to be mainly considered when elaborating urban traffic models and travel plans. The advantage of using the proposed logit models and utility function is the ability to identify the relationship among the travel behavior of an individual and the mode choice. With the results, it is possible to estimate the influence of the various variables on mode choice and identify the best mode based on the utility function. Full article

(This article belongs to the Special Issue Modeling Activity-Travel Behavior for Sustainable Transportation)

► Show Figures

Figure 1

20 pages, 1496 KiB

Open AccessArticle

Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

by Jiyuan Tan, Qianqian Qiu, Weiwei Guo and Tingshuai Li

Sustainability 2021, 13(6), 3191; https://doi.org/10.3390/su13063191 - 15 Mar 2021

Cited by 31 | Viewed by 4603

Abstract

The integration of multi-source transportation data is complex and insufficient in most of the big cities, which made it difficult for researchers to conduct in-depth data mining to improve the policy or the management. In order to solve this problem, a top-down approach is used to construct a knowledge graph of urban traffic system in this paper. First, the model layer of the knowledge graph was used to realize the reuse and sharing of knowledge. Furthermore, the model layer then was stored in the graph database Neo4j. Second, the representation learning based knowledge reasoning model was adopted to implement knowledge completion and improve the knowledge graph. Finally, the proposed method was validated with an urban traffic data set and the results showed that the model could be used to mine the implicit relationship between traffic entities and discover traffic knowledge effectively. Full article

(This article belongs to the Special Issue Modeling Activity-Travel Behavior for Sustainable Transportation)

► Show Figures

Figure 1

21 pages, 1059 KiB

Open AccessArticle

Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies

by Yiming Bie, Mingjie Hao and Mengzhu Guo

Sustainability 2021, 13(4), 1827; https://doi.org/10.3390/su13041827 - 08 Feb 2021

Cited by 29 | Viewed by 3036

Abstract

The emission of greenhouse gases from public transportation has aroused extensive public attention in recent years. Electric buses have the advantage of zero emission, which could prevent the further deterioration of environmental problems. Since 2018, the number of electric buses has exceeded that of traditional buses. Thus, it is an inevitable trend for the sustainable development of the automobile industry to replace traditional fuel buses, and developing electric buses is an important measure to relieve traffic congestion. Furthermore, the bus scheduling has a significant impact on passenger travel times and operating costs. It is common that passenger demand at different stops is uneven in a public transportation system. Since applying all-stop scheduling only cannot match the passenger demand of some stops with bus resources, this paper proposes an integrated all-stop and short-turning service for electric buses, reducing the influence of uneven ridership on load factor to enhance transit attractiveness. Simultaneously, considering the time-of-use pricing strategy used by the power sector, the combinational charging strategy of daytime and overnight is proposed to reduce electricity costs. Finally, the branch-and-price algorithm is adopted to solve this problem. Compared with all-stop scheduling, the results demonstrate a reduction of 13.5% in total time cost under the combinational scheduling. Full article

(This article belongs to the Special Issue Modeling Activity-Travel Behavior for Sustainable Transportation)

► Show Figures

Figure 1

17 pages, 2867 KiB

Open AccessArticle

Driver Distraction Recognition Using Wearable IMU Sensor Data

by Wencai Sun, Yihao Si, Mengzhu Guo and Shiwu Li

Sustainability 2021, 13(3), 1342; https://doi.org/10.3390/su13031342 - 28 Jan 2021

Cited by 8 | Viewed by 2344

Abstract

Distracted driving has become a major cause of road traffic accidents. There are generally four different types of distractions: manual, visual, auditory, and cognitive. Manual distractions are the most common. Previous studies have used physiological indicators, vehicle behavior parameters, or machine-visual features to support research. However, these technologies are not suitable for an in-vehicle environment. To address this need, this study examined a non-intrusive method for detecting in-transit manual distractions. Wrist kinematics data from 20 drivers were collected using wearable inertial measurement units (IMU) to detect four common gestures made while driving: dialing a hand-held cellular phone, adjusting the audio or climate controls, reaching for an object in the back seat, and maneuvering the steering wheel to stay in the lane. The study proposed a progressive classification model for gesture recognition, including two major time-based sequencing components and a Hidden Markov Model (HMM). Results show that the accuracy for detecting disturbances was 95.52%. The accuracy associated with recognizing manual distractions reached 96.63%, using the proposed model. The overall model has the advantages of being sensitive to perceptions of motion, effectively solving the problem of a fall-off in recognition performance due to excessive disturbances in motion samples. Full article

(This article belongs to the Special Issue Modeling Activity-Travel Behavior for Sustainable Transportation)

► Show Figures

Journal Menu

Journal Browser

Modeling Activity-Travel Behavior for Sustainable Transportation

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI