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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 13804

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Special Issue Editor

Dr. Zhichao Cao

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Guest Editor

School of Transportation and Civil Engineering, Nantong University, Nantong 226000, China
Interests: transportation planning and management; timetabling; vehicle/train scheduling; passenger demand control; autonomous public transport vehicle development

Special Issue Information

Dear Colleagues,

The focus of the Special Issue "New Advances in Transportation Planning and Management to Facilitate Public Health and Environment" is to promote improved public health and emerging environmentally friendly transport-integrated development. In the scope of this Special Issue, research studies, scientific applications, overviews and historical literature reviews in the fields of transportation, the environment and public health are welcomed. Specifically, the interests of this Special Issue include, but are not limited to, the underlying concepts:

Planning level: transportation network design; passenger demand prediction; timetables; vehicle/train schedules; driver schedules; recharging infrastructure.
Operation/management level: energy-efficiency; user safety; control under the impact of COVID-19; maintenance; rescheduling; synchronization; speed adjustment; skip-stop; rolling stock or fleet size; user equilibrium; traffic flow theories; pedestrian dynamics; transit operational stability, spatial interaction network analysis; airport and air traffic; traffic environments.
Technical level: programming models; optimization problems; heuristic; exact algorithm; graph theory; game theory; in-vehicle assistant system; interdisciplinary application in transportation.
Future mobility: multimodal transportation; intelligent transportation systems; demand response transport; unmanned aerial vehicle route; public health.

Dr. Zhichao Cao
Guest Editor

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.

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Keywords

transportation planning
optimization modeling
demand management
advanced technique
autonomous/emerging mobility

Published Papers (9 papers)

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Research

15 pages, 9617 KiB

Open AccessArticle

Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section

by Mariusz Korzeń and Maciej Kruszyna

Int. J. Environ. Res. Public Health 2023, 20(6), 4960; https://doi.org/10.3390/ijerph20064960 - 11 Mar 2023

Viewed by 1177

Abstract

The railway is one of the most energy-efficient modes of transport, helping to enhance the environment and public health in cities and agglomerations. In this paper, the authors raise the issue of the construction of an underground railway route in Wrocław (Poland) to allow the organization of the suburban rail system in the agglomeration. There are many concepts for the construction of this route, but so far none has been realized. Therefore, it is important to design the route properly. Here, five options for this tunnel are considered and evaluated. To make such an evaluation, the authors construct a modified ant colony optimization algorithm (ACO). The “classic” algorithm considers the determination of the shortest route. The modification of the algorithm will allow a more accurate analysis of the issue, taking into account more parameters than just the length of the route. These are the location of traffic generators in the city center, the number of inhabitants neighboring the stations, and the number of tram or bus lines integrated with the railway. The presented method and exemplary case study should allow for the evaluation, introduction, or development of the city railway. Full article

(This article belongs to the Special Issue New Advances in Transportation Planning and Management to Facilitate Public Health and Environment)

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22 pages, 3102 KiB

Open AccessArticle

Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing–Tianjin–Hebei Urban Agglomeration, China

by Shuai Yu, Bin Li and Dongmei Liu

Int. J. Environ. Res. Public Health 2023, 20(2), 1416; https://doi.org/10.3390/ijerph20021416 - 12 Jan 2023

Cited by 1 | Viewed by 1386

Abstract

The outbreak and spreading of COVID-19 since early 2020 have dramatically impacted public health and the travel environment. However, most of the studies are devoted to travel behavior from the macro perspective. Meanwhile, few researchers pay attention to intercity travel behavior. Thus, this study explores the changes in the travel behavior of intercity high-speed railway travelers during the COVID-19 pandemic from the perspective of the individual. Using the smartphone data, this study first extracts the trip chains by proposing a novel method including three steps. The trip chain can describe the whole process of traveling, including individual characteristics, travel time, travel distance, travel mode, etc. Then, a Multinomial Logit model is applied to analyze the trip chains which verified the validity by using studentized residual error. The study finds that intercity travel behavior has changed in gender, age, travel mode choice, and travel purpose by comparing the trip chains between May 2019 and May 2021 in the Beijing–Tianjin–Hebei urban agglomeration. The method proposed in this study can be used to assess the impact of any long-term emergency on individual travel behavior. The findings proposed in this study are expected to guide public health management and travel environment improvement under the situation of normalized COVID-19 prevention and safety control. Full article

(This article belongs to the Special Issue New Advances in Transportation Planning and Management to Facilitate Public Health and Environment)

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16 pages, 3634 KiB

Open AccessArticle

Invulnerability of the Urban Agglomeration Integrated Passenger Transport Network under Emergency Events

by Peng Wu, Yunfei Li and Chengbing Li

Int. J. Environ. Res. Public Health 2023, 20(1), 450; https://doi.org/10.3390/ijerph20010450 - 27 Dec 2022

Cited by 3 | Viewed by 1419

Abstract

Urgent natural environmental events, such as floods, power failures, and epidemics, result in disruptions to the traffic system and heavy disturbances in public requirements. In order to strengthen the ability of the transport network to handle urgent natural environmental issues, this paper simulates the disruption situation of traffic stations in the urban agglomeration by attacking nodes, and evaluates the ability of the transport network to resist disruptions (i.e., invulnerability). Firstly, the model of the urban agglomeration integrated passenger transport network is established based on complex network theory. The highway network, railway network, and coupling network are combined into a multi-layer network space structure, and the edge weight is calibrated by travel time and cost. Secondly, the invulnerability simulation process including multiple attack modes under random and deliberate attack strategies is sorted out. By improving the traditional network efficiency indicator, the network impedance efficiency indicator is proposed to measure network performance, and the network relative impedance efficiency indicator is used to evaluate network invulnerability and identify key nodes. Finally, Chengdu–Chongqing urban agglomeration is taken as a case study. The results show that the network does not collapse quickly and it shows certain invulnerability and robustness under continuous random attacks. Network performance and invulnerability are not necessarily positively correlated. The failure of individual nodes that are small in scale but act as transit hubs may significantly degrade the network performance. The identified key nodes have significance for guiding the construction, maintenance, and optimization of the urban agglomeration passenger transport network, which is conducive to promoting public safety. Full article

(This article belongs to the Special Issue New Advances in Transportation Planning and Management to Facilitate Public Health and Environment)

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17 pages, 1040 KiB

Open AccessArticle

Determinants and the Moderating Effects of Individual Characteristics on Autonomous Vehicle Adoption in China

by Tianpei Tang, Xiwei Wang, Jianbing Wu, Meining Yuan, Yuntao Guo and Xunqian Xu

Int. J. Environ. Res. Public Health 2023, 20(1), 43; https://doi.org/10.3390/ijerph20010043 - 20 Dec 2022

Cited by 2 | Viewed by 1726

Abstract

Along with the increasing popularity of autonomous vehicles (AVs), urban livability and public health will be enhanced due to ecofriendly issues: alleviated traffic congestion, lower car ownership, and reduced transport emissions. However, some emerging issues, including public safety, trust, privacy, reliability, underdeveloped legislation, and liability, may deter user intentions to adopt an AV. This study introduces an extensive factor, playfulness, into the technology acceptance model (TAM) to quantify the impacts of psychological factors (perceived usefulness, perceived ease of use, and perceived playfulness) on AV adoption intention. This study proposes four AV-related policy measures (financial incentivization, information dissemination, convenience, and legal normalization) and examines how policy measures motivate users to adopt an AV to facilitate public safety. Furthermore, this study investigated the moderating effects of demographic factors on the relationships between independent variables and AV adoption intention. Two models were proposed and estimated using a total of 1831 survey responses in China. The psychology-related and policy-related models explained 62.2% and 33.6% of the variance in AV adoption intention, respectively. The results suggest that perceived playfulness (β = 0.524, p < 0.001) and information dissemination (β = 0.348, p < 0.001) are the most important influencing factors of AV adoption intention. In addition, demographic factors (gender, education, income, the number of private cars owned by a family, and types of cities) can moderate the effects of psychological factors and policy measures on user intentions to adopt an AV. These insights can be employed to design more cost-effective policies and strategies for subgroups of the population to maximize the AV adoption intention. Full article

(This article belongs to the Special Issue New Advances in Transportation Planning and Management to Facilitate Public Health and Environment)

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16 pages, 1729 KiB

Open AccessArticle

Modeling and Simulation of Crowd Pre-Evacuation Decision-Making in Complex Traffic Environments

by Zhihong Li, Shiyao Qiu, Xiaoyu Wang and Li Zhao

Int. J. Environ. Res. Public Health 2022, 19(24), 16664; https://doi.org/10.3390/ijerph192416664 - 12 Dec 2022

Viewed by 1389

Abstract

Human movements in complex traffic environments have been successfully simulated by various models. It is crucial to improve crowd safety and urban resilience. However, few studies focus on reproducing human behavior and predicting escape reaction time in the initial judgement stage in complex traffic environments. In this paper, a pedestrian pre-evacuation decision-making model considering pedestrian heterogeneity is proposed for complex environments. Firstly, the model takes different obvious factors into account, including cognition, information, experience, habits, stress, and decision-making ability. Then, according to the preference of the escapees, the personnel decision-making in each stage is divided into two types: stay and escape. Finally, multiple influencing factors are selected to construct the regression equation for prediction of the escape opportunity. The results show that: (1) Choices of escape opportunity are divided into several stages, which are affected by the pedestrian individual risk tolerance, risk categories strength, distance from danger, and reaction of the neighborhood crowd. (2) There are many important factors indicating the pedestrian individual risk tolerance, in which Gen, Group, Time and Mode are a positive correlation, while Age and Zone are a negative correlation. (3) The analysis of the natural response rate of different evacuation strategies shows that 19.81% of people evacuate immediately. The research in this paper can better protect public safety and promote the normal activities of the population. Full article

(This article belongs to the Special Issue New Advances in Transportation Planning and Management to Facilitate Public Health and Environment)

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26 pages, 11039 KiB

Open AccessArticle

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

by Jinrui Zang, Pengpeng Jiao, Guohua Song, Zhihong Li and Tingyi Peng

Int. J. Environ. Res. Public Health 2022, 19(24), 16524; https://doi.org/10.3390/ijerph192416524 - 09 Dec 2022

Viewed by 1334

Abstract

Vehicle emissions seriously affect the air environment and public health. The dynamic estimation method of vehicle emissions changing over time on the road network has always been the bottleneck of air quality simulation. The dynamic traffic volume is one of the important parameters to estimate vehicle emission, which is difficult to obtain effectively. A novel estimation method of whole sample traffic volumes and emissions on the entire road network based on multifactor Macroscopic Fundamental Diagram (MFD) is proposed in this paper. First, the intelligent clustering and recognition methods of traffic flow patterns are constructed based on neural network and deep-learning algorithms. Then, multifactor MFD models are developed considering different road types, traffic flow patterns and weekday peak hours. Finally, the high spatiotemporal resolution estimation method of whole sample traffic volumes and emissions are constructed based on MFD models. The results show that traffic flow patterns are clustered efficiently by the Self-Organizing Maps (SOM) algorithm combined with the direct time-varying speed index, which describe 91.7% traffic flow states of urban roads. The Deep Belief Network (DBN) algorithm precisely recognizes 92.1% of the traffic patterns based on the speeds of peak hours. Multifactor MFD models estimate the whole sample traffic volumes with a high accuracy of 91.6%. The case study shows that the vehicle emissions are evaluated dynamically based on the novel estimation method proposed in this paper, which is conducive to the coordinated treatment of air pollution. Full article

(This article belongs to the Special Issue New Advances in Transportation Planning and Management to Facilitate Public Health and Environment)

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19 pages, 2629 KiB

Open AccessArticle

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems

by Lu Zeng, Zinuo Li, Jie Yang and Xinyue Xu

Int. J. Environ. Res. Public Health 2022, 19(24), 16433; https://doi.org/10.3390/ijerph192416433 - 07 Dec 2022

Cited by 6 | Viewed by 1481

Abstract

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R², respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation. Full article

(This article belongs to the Special Issue New Advances in Transportation Planning and Management to Facilitate Public Health and Environment)

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19 pages, 703 KiB

Open AccessArticle

IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant

by Tianbo Ji, Xuanhua Yin, Peng Cheng, Liting Zhou, Siyou Liu, Wei Bao and Chenyang Lyu

Int. J. Environ. Res. Public Health 2022, 19(23), 15493; https://doi.org/10.3390/ijerph192315493 - 22 Nov 2022

Viewed by 1365

Abstract

An advanced driver simulator methodology facilitates a well-connected interaction between the environment and drivers. Multiple traffic information environment language processing aims to help drivers accommodate travel demand: safety prewarning, destination navigation, hotel/restaurant reservation, and so on. Task-oriented dialogue systems generally aim to assist human users in achieving these specific goals by a conversation in the form of natural language. The development of current neural network based dialogue systems relies on relevant datasets, such as KVRET. These datasets are generally used for training and evaluating a dialogue agent (e.g., an in-vehicle assistant). Therefore, a simulator for the human user side is necessarily required for assessing an agent system if no real person is involved. We propose a new end-to-end simulator to operate as a human driver that is capable of understanding and responding to assistant utterances. This proposed driver simulator enables one to interact with an in-vehicle assistant like a real person, and the diversity of conversations can be simply controlled by changing the assigned driver profile. Results of our experiment demonstrate that this proposed simulator achieves the best performance on all tasks compared with other models. Full article

(This article belongs to the Special Issue New Advances in Transportation Planning and Management to Facilitate Public Health and Environment)

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20 pages, 2823 KiB

Open AccessArticle

An Integrated Multi-Objective Optimization for Dynamic Airport Shuttle Bus Location, Route Design and Departure Frequency Setting Problem

by Ming Wei, Congxin Yang and Tao Liu

Int. J. Environ. Res. Public Health 2022, 19(21), 14469; https://doi.org/10.3390/ijerph192114469 - 04 Nov 2022

Cited by 3 | Viewed by 1448

Abstract

An airport shuttle bus (ASB), as an environmentally friendly mode of green transportation, is an effective way to solve the “first/last mile” of aviation passengers, which can attract a higher passenger transfer from private cars to public transport, thereby reducing emissions of carbon dioxide and other polluting gases. This study presents a multi-objective mixed-integer linear programming for ASB services in a dynamic environment. Taking into account time-varying demand and travel time characteristics in different periods, the proposed model provides a comprehensive framework that simultaneously advises passengers to join the bus at the nearest bus stations, designs routes for transporting them from these selected stations through the airport, and computes their departure frequencies in multiple periods. The primary objective is to optimize both the total ride time and waiting time for all passengers. The secondary objective is to optimize the total transfer distance of all passengers simultaneously. Given the Non-Deterministic Polynomial (NP) hardness of this problem, a two-stage multi-objective heuristic approach based on the non-dominated sorting genetic algorithm (NSGA-II) is combined with a dynamic programming search method and further advanced to obtain the Pareto-optimal solutions of the proposed model within a reasonable time. Finally, the proposed model and algorithm feasibility are proved by a test example of designing a shuttle bus route and schedule at Tianjin Airport, China. The results show that the total passenger travel time of the presented model is markedly reduced by 1.21% compared with the conventional model. Full article

(This article belongs to the Special Issue New Advances in Transportation Planning and Management to Facilitate Public Health and Environment)

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