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Emerging Research in Urban Computing and Intelligent Transport Systems

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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 25 July 2024 | Viewed by 25833

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Special Issue Editors

Prof. Dr. Jianbo Li

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Guest Editor

College of Computer Science and Technology, Qingdao University, Qingdao, China
Interests: urban computing; smart transportation; data triage in opportunity networks

Dr. Junjie Pang

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Guest Editor

College of Computer Science and Technology, Qingdao University, Qingdao, China
Interests: federated learning; reinforcement learning; next-generation networking

Dr. Antonio Comi

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Guest Editor

Department of Enterprise Engineering, University of Rome Tor Vergata, Rome, Italy
Interests: city logistics; freight transportation; demand forecasting; intelligent transport system
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Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the study of the latest research findings and practical applications of urban computing and intelligent transportation systems (ITS). This Special Issue addresses urban computing and ITS across numerous technical aspects of ITS technology that involve people, vehicles, items, information technology, and physical infrastructures, all of which interact in complex ways. We welcome submissions of original, unpublished, and novel in-depth studies that make significant methodological or applied contributions to the field. Potential topics of interest include, but are not limited to, the following topics:

Urban computing based on artificial intelligence algorithms;
Urban computing based on statistical methods;
Solutions for sustainable transportation/urban development;
Data integration and analysis;
Information collection and processing;
Image processing applications in ITS;
Autonomous vehicles;
Traffic flow management and control;
Innovative algorithms in urban computing/ITS;
Networks and Communications in ITS;
Public transportation system technologies;
Public transport logistics;
Urban emergency and incident management;
Urban demand management;
ITS industrial applications;
Urban health check.

Prof. Dr. Jianbo Li
Dr. Junjie Pang
Prof. Dr. Antonio Comi
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. Information is an international peer-reviewed open access monthly 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 1600 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

urban computing
intelligent transportation systems

Published Papers (11 papers)

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Research

24 pages, 4184 KiB

Open AccessArticle

Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

by Bohdan Petryshyn, Serhii Postupaiev, Soufiane Ben Bari and Armantas Ostreika

Information 2024, 15(2), 113; https://doi.org/10.3390/info15020113 - 15 Feb 2024

Viewed by 1571

Abstract

The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is an underexplored research area. Amazon Web Services (AWS) DeepRacer emerges as a powerful infrastructure for engineering and analysing autonomous models, providing a robust foundation for addressing these complexities. This research investigates the feasibility of training end-to-end self-driving models focused on obstacle avoidance using reinforcement learning on the AWS DeepRacer autonomous race car platform. A comprehensive literature review of autonomous driving methodologies and machine learning model architectures is conducted, with a particular focus on object avoidance, followed by hands-on experimentation and the analysis of training data. Furthermore, the impact of sensor choice, reward function, action spaces, and training time on the autonomous obstacle avoidance task are compared. The results of the best configuration experiment demonstrate a significant improvement in obstacle avoidance performance compared to the baseline configuration, with a 95.8% decrease in collision rate, while taking about 79% less time to complete the trial circuit. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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13 pages, 2755 KiB

Open AccessArticle

Measuring Trajectory Similarity Based on the Spatio-Temporal Properties of Moving Objects in Road Networks

by Ali Dorosti, Ali Asghar Alesheikh and Mohammad Sharif

Information 2024, 15(1), 51; https://doi.org/10.3390/info15010051 - 17 Jan 2024

Viewed by 1148

Abstract

Advancements in navigation and tracking technologies have resulted in a significant increase in movement data within road networks. Analyzing the trajectories of network-constrained moving objects makes a profound contribution to transportation and urban planning. In this context, the trajectory similarity measure enables the discovery of inherent patterns in moving object data. Existing methods for measuring trajectory similarity in network space are relatively slow and neglect the temporal characteristics of trajectories. Moreover, these methods focus on relatively small volumes of data. This study proposes a method that maps trajectories onto a network-based space to overcome these limitations. This mapping considers geographical coordinates, travel time, and the temporal order of trajectory segments in the similarity measure. Spatial similarity is measured using the Jaccard coefficient, quantifying the overlap between trajectory segments in space. Temporal similarity, on the other hand, incorporates time differences, including common trajectory segments, start time variation and trajectory duration. The method is evaluated using real-world taxi trajectory data. The processing time is one-quarter of that required by existing methods in the literature. This improvement allows for spatio-temporal analyses of a large number of trajectories, revealing the underlying behavior of moving objects in network space. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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11 pages, 1086 KiB

Open AccessArticle

Resource Allocation in Decentralized Vehicular Edge Computing Network

by Hongli Zhang and Ying Li

Information 2023, 14(4), 206; https://doi.org/10.3390/info14040206 - 27 Mar 2023

Viewed by 1153

Abstract

Computation-intensive vehicle tasks sharply increase with the rapid growth of intelligent vehicles. The technology of Mobile Edge Computing (MEC) has the possibility of assisting vehicles with computation offloading. To solve the problem of computation resource management and guarantee the security of resource transaction, we jointly combine the MEC network and the blockchain networks to build a blockchain based MEC offloading model. The non-cooperative interactions between MEC server and vehicles formulate a two-stage Stackelberg game in an aim to maximize their benefits and information security. We theoretically demonstrate the unique existence of Nash equilibrium, which enables participants to decide their optimal strategies. Finally, the performance of the proposed model is analyzed by conducting simulation experiments. Our proposed model optimizes resource allocation and also improves the security of the whole network. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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23 pages, 6031 KiB

Open AccessArticle

Information and Entropy Aspects of the Specifics of Regional Road Traffic Accident Rate in Russia

by Artur I. Petrov

Information 2023, 14(2), 138; https://doi.org/10.3390/info14020138 - 20 Feb 2023

Cited by 3 | Viewed by 2071

Abstract

The aim of this research is to study the specifics of the road accident rate formation processes in regions of the Russian Federation (2021) using information-entropic analysis. The typical research approaches (correlation-regression, factorial analyses, simulation modelling, etc.) do not always allow us to identify its specificity. It is impossible to evaluate the quality of the researched process’s structure using these methods. However, this knowledge is required to understand the distinctions between high-quality road safety management and its opposite. In order to achieve the goal of the research methodology based on the use of the classical approaches of C. Shannon, the quantitative value of information entropy H was elaborated. The key components of this method are the modelling of the cause-and-effect chain of road accident rate formation and the consideration of the relative significances of individual blocks of the process in achieving the final result. During the research the required statistical data were collected and the structure of the road accident rate formation process in 82 regions of the Russian Federation in the format “Population P—Fleet of vehicles N_Vh—Road Traffic Accidents N_RA—RTA Victims N_V—Fatality Cases N_D” was analyzed. The fact that the structure of the road accident rate formation process is extremely specific in different Russian regions was shown. Exactly this specificity forms the degree of ambiguity in the state of Russian regional road safety provision systems in terms of the probability of death in road accidents. The main conclusion of this research is that information-entropic analysis can be successfully used to assess the structural quality of road safety systems. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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13 pages, 4182 KiB

Open AccessArticle

Research on Traffic Congestion Forecast Based on Deep Learning

by Yangyang Qi and Zesheng Cheng

Information 2023, 14(2), 108; https://doi.org/10.3390/info14020108 - 09 Feb 2023

Cited by 7 | Viewed by 3185

Abstract

In recent years, the rapid economic development of China, the increase of the urban population, the continuous growth of private car ownership, the uneven distribution of traffic flow, and the local congestion of the road network have caused traffic congestion. Traffic congestion has become an inevitable problem in the process of urban development, bringing hazards and hidden dangers to citizens’ travel and urban development. The management of traffic congestion first lies in the accurate completion of the identification of road traffic status and the need to predict road congestion in the city, so as to improve the use rate of urban infrastructure road facilities and better alleviate road congestion. In this study, a deep spatial and temporal network model (DSGCN) for predicting traffic congestion status is proposed. First, our study divides the traffic network into grids, where each grid represents a different independent region. In this paper, the centroids of the grid regions are abstracted as nodes, and the dynamic correlations between the nodes are expressed in the form of adjacency matrix. Then, Graph Convolutional Neural Network is used to capture the spatial correlation between regions and a two-layer long and short-term feature model (DSTM) is used to capture the temporal correlation between regions. Finally, the DSGCN outperforms other baseline models and has higher accuracy for traffic congestion prediction as demonstrated by experiments on real PeMS datasets. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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18 pages, 4339 KiB

Open AccessArticle

An Online Optimal Bus Signal Priority Strategy to Equalise Headway in Real-Time

by Xuehao Zhai, Fangce Guo and Rajesh Krishnan

Information 2023, 14(2), 101; https://doi.org/10.3390/info14020101 - 06 Feb 2023

Cited by 1 | Viewed by 1370

Abstract

Bus bunching is a severe problem that affects the service levels of public transport systems. Most of the previous studies in the field of Bus Signal Priority (BSP) and Transit Signal Priority (TSP) focus on reducing a bus delay at signalised intersections and ignore the importance of balancing the bus headways. However, since general BSP methods allocate uneven priorities for individual buses, the headways of bus sequences are prioritised or delayed randomly, increasing the likelihood of bus bunching. To address this problem and to improve the reliability of bus services, we propose an online optimisation model to determine the signal duration and splits for each traffic intersection and each signal cycle for bus priority. The proposed model is able to induce the signal timing back to a baseline when the BSP request frequency is low. Using the proposed model, a statistically significant reduction of 10.0% was achieved for bus headway deviation and 6.4% for passenger waiting times. The simulation-based evaluation results also indicate that the proposed model does not affect the efficiency of bus services and other vehicles significantly. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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14 pages, 2514 KiB

Open AccessArticle

Prediction and Privacy Scheme for Traffic Flow Estimation on the Highway Road Network

by Mohammed Akallouch, Oussama Akallouch, Khalid Fardousse, Afaf Bouhoute and Ismail Berrada

Information 2022, 13(8), 381; https://doi.org/10.3390/info13080381 - 09 Aug 2022

Cited by 1 | Viewed by 1919

Abstract

Accurate and timely traffic information is a vital element in intelligent transportation systems and urban management, which is vitally important for road users and government agencies. However, existing traffic prediction approaches are primarily based on standard machine learning which requires sharing direct raw information to the global server for model training. Further, user information may contain sensitive personal information, and sharing of direct raw data may lead to leakage of user private data and risks of exposure. In the face of the above challenges, in this work, we introduce a new hybrid framework that leverages Federated Learning with Local Differential Privacy to share model updates rather than directly sharing raw data among users. Our FL-LDP approach is designed to coordinate users to train the model collaboratively without compromising data privacy. We evaluate our scheme using a real-world public dataset and we implement different deep neural networks. We perform a comprehensive evaluation of our approach with state-of-the-art models. The prediction results of the experiment confirm that the proposed scheme is capable of building performance accurate traffic predictions, improving privacy preservation, and preventing data recovery attacks. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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15 pages, 1457 KiB

Open AccessEditor’s ChoiceArticle

Sustainable Mobility as a Service: Demand Analysis and Case Studies

by Giuseppe Musolino

Information 2022, 13(8), 376; https://doi.org/10.3390/info13080376 - 05 Aug 2022

Cited by 15 | Viewed by 2578

Abstract

Urban mobility is evolving today towards the concept of Mobility as a Service (MaaS). MaaS allows passengers to use different transport services as a single option, by using a digital platform. Therefore, according to the MaaS concept, the mobility needs of passengers are the central element of the transport service. The objective of this paper is to build an updated state-of-the-art of the main disaggregated and aggregated variables connected to travel demand in presence of MaaS. According to the above objective, this paper deals with methods and case studies to analyze passengers’ behaviour in the presence of MaaS. The methods described rely on the Transportation System Models (TSMs), in particular with the travel demand modelling component. The travel demand may be estimated by means of disaggregated, or sample, surveys (e.g., individual choices) and of aggregate surveys (e.g., characteristics of the area, traffic flows). The surveys are generally supported by Information Communication System (ICT) tools, such as: smartphones; smartcards; Global Position Systems (GPS); points of interest. The analysis of case studies allows to aggregate the existing scientific literature according to some criteria: the choice dimension of users (e.g., mode, bundle and path, or a combination of them); the characteristics of the survey (e.g., revealed preferences or stated preferences); the presence of behavioural theoretical background and of calibrated choice model(s). Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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25 pages, 1840 KiB

Open AccessEditor’s ChoiceArticle

Sustainable Mobility as a Service: Dynamic Models for Agenda 2030 Policies

by Francesco Russo

Information 2022, 13(8), 355; https://doi.org/10.3390/info13080355 - 25 Jul 2022

Cited by 17 | Viewed by 2449

Abstract

Growth trends in passenger transport demand and gross domestic product have so far been similar. The increase in mobility in one area is connected with the increase in GDP in the same area. This increase is representative of the economic and social development of the area. At the same time, the increase in mobility produces one of the most negative environmental impacts, mainly determined by the growth of mobility of private cars. International attention is given to the possibilities of increasing mobility and, therefore, social and economic development without increasing environmental impacts. One of the most promising fields is that of MaaS: Mobility as a Service. MaaS arises from the interaction of new user behavioral models (demand) and new decision-making models on services (supply). Advanced interaction arises from the potentialities allowed by emerging ICT technologies. There is a delay in the advancement of transport system models that consider the updating of utility and choice for the user by means of updated information. The paper introduces sustainability as defined by Agenda 2030 with respect to urban passenger transport, then examines the role of ICT in the development of MaaS formalizing a dynamic model of demand–supply interaction explicating ICT. Finally, the advanced Sustainable MaaS, defined SMaaS, is analyzed, evidencing the contribution to achieving the goals of Agenda 2030. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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19 pages, 2101 KiB

Open AccessEditor’s ChoiceArticle

Sustainable Mobility as a Service: Supply Analysis and Test Cases

by Corrado Rindone

Information 2022, 13(7), 351; https://doi.org/10.3390/info13070351 - 21 Jul 2022

Cited by 22 | Viewed by 3017

Abstract

Urban mobility is one of the main issues in the pursuit of sustainability. The United Nations 2030 Agenda assigns mobility and transport central roles in sustainable development and its components: economic, social, and environment. In this context, the emerging concept of Mobility as a Service (MaaS) offers an alternative to unsustainable mobility, often based on private car use. From the point of view of sustainable mobility, the MaaS paradigm implies greater insights into the transport system and its components (supply, demand, and reciprocal interactions). This paper proposes an approach to the transport system aimed at overcoming the current barriers to the implementation of the paradigm. The focus is on the implications for the transport supply subsystem. The investigation method is based on the analysis of the main components of such subsystem (governance, immaterial, material, equipment) and its role in the entire transport system. Starting with the first experiences of Finnish cities, the paper investigates some real case studies, which are experimenting with MaaS, to find common and uncommon elements. From the analyses, it emerges that the scientific literature and real experiences mainly focus on the immaterial components alone. To address the challenges related to sustainable mobility, this paper underlines the need to consider all components within a transport system approach. The findings of the paper are useful in several contexts. In the context of research, the paper offers an analysis of the transport supply system from the point of view of the MaaS paradigm. In the real context, the paper offers further useful insights for operators and decision-makers who intend to increase the knowledge and skills necessary to face challenges related to the introduction of MaaS. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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15 pages, 2061 KiB

Open AccessEditor’s ChoiceArticle

Sustainable Mobility as a Service: Framework and Transport System Models

by Antonino Vitetta

Information 2022, 13(7), 346; https://doi.org/10.3390/info13070346 - 16 Jul 2022

Cited by 22 | Viewed by 3473

Abstract

Passenger mobility plays an important role in today’s society and optimized transport services are a priority. In recent years, MaaS (Mobility as a Service) has been studied and tested as new integrated services for users. In this paper, MaaS is studied considering the sustainability objectives and goals to be achieved with particular reference to the consolidated methodologies adopted in the transport systems engineering for design, management, and monitoring of transport services; it is defined as Sustainable MaaS (S-MaaS). This paper considers the technological and communication platform essential and assumed to be a given considering that it has been proposed in many papers and it has been tested in some areas together with MaaS. Starting from the MaaS platform, the additional components and models necessary for the implementation of an S-MaaS are analyses in relation to: a Decision Support System (DSS) that supports MaaS public administrations and MaaS companies for the design of the service and demand management; a system for the evaluation of intervention policies; and also considers smart planning for a priori and a posteriori evaluation of sustainability objectives and targets. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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