Research

13 pages, 2359 KiB

Open AccessArticle

Locating Electrified Aircraft Service to Reduce Urban Congestion

by Raj Bridgelall

Information 2024, 15(4), 186; https://doi.org/10.3390/info15040186 - 29 Mar 2024

Viewed by 587

The relentless expansion of urban populations and the surge in e-commerce have increased the demand for rapid delivery services, leading to an increase in truck traffic that contributes to urban congestion, environmental pollution, and economic inefficiencies. The critical challenge this poses is not [...] Read more.

The relentless expansion of urban populations and the surge in e-commerce have increased the demand for rapid delivery services, leading to an increase in truck traffic that contributes to urban congestion, environmental pollution, and economic inefficiencies. The critical challenge this poses is not only in managing urban spaces efficiently but also in aligning with global sustainability goals. This study addresses the pressing need for innovative solutions to reduce reliance on truck transportation in congested urban areas without compromising the efficiency of freight delivery systems. This study contributes a novel approach that leverages electrified and autonomous aircraft (EAA) cargo shuttles to shift the bulk of air transportable freight from road to air, specifically targeting underutilized airports and establishing vertiports in remote locations. By applying data mining techniques to analyze freight flow data, this research identifies key commodity categories and metropolitan statistical areas (MSAs) where the implementation of EAA services could significantly mitigate truck-induced congestion. The findings reveal that targeting a select few commodities and MSAs can potentially decrease truck traffic, with electronics emerging as the dominant commodity category, and cities like Los Angeles and Chicago as prime candidates for initial EAA service deployment. Stakeholders in urban planning, transportation logistics, and environmental policy will find this study’s insights beneficial. This work lays a foundation for future innovations in sustainable urban mobility and logistics. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

► Show Figures

Graphical abstract

17 pages, 1420 KiB

Open AccessArticle

Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts

by Francis Marco Maria Cirianni, Antonio Comi and Agata Quattrone

Information 2023, 14(10), 581; https://doi.org/10.3390/info14100581 - 21 Oct 2023

Cited by 2 | Viewed by 3330

Abstract

The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) [...] Read more.

The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) and to identify network and service configurations by comparing phenomena in similar contexts, as well as support the implementation of measures for managing demand that achieve sustainable goals. In this paper, an in-depth analysis of scenarios, with an IT (Information Technology) framework based on emerging technologies and AI to support sustainable and cooperative digital mobility, is provided. Therefore, the definition of the functional architecture of an AI-based mobility control centre is defined, and the process that has been implemented in a medium-large city is presented. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

► Show Figures

Figure 1

19 pages, 3140 KiB

Open AccessArticle

A Hybrid Univariate Traffic Congestion Prediction Model for IoT-Enabled Smart City

by Ayushi Chahal, Preeti Gulia, Nasib Singh Gill and Ishaani Priyadarshini

Information 2023, 14(5), 268; https://doi.org/10.3390/info14050268 - 30 Apr 2023

Cited by 2 | Viewed by 1923

Abstract

IoT devices collect time-series traffic data, which is stochastic and complex in nature. Traffic flow prediction is a thorny task using this kind of data. A smart traffic congestion prediction system is a need of sustainable and economical smart cities. An intelligent traffic [...] Read more.

IoT devices collect time-series traffic data, which is stochastic and complex in nature. Traffic flow prediction is a thorny task using this kind of data. A smart traffic congestion prediction system is a need of sustainable and economical smart cities. An intelligent traffic congestion prediction model using Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Bidirectional Long Short-Term Memory (Bi-LSTM) is presented in this study. The novelty of this model is that the proposed model is hybridized using a Back Propagation Neural Network (BPNN). Instead of traditionally presuming the relationship of forecasted results of the SARIMA and Bi-LSTM model as a linear relationship, this model uses BPNN to discover the unknown function to establish a relation between the forecasted values. This model uses SARIMA to handle linear components and Bi-LSTM to handle non-linear components of the Big IoT time-series dataset. The “CityPulse EU FP7 project” is a freely available dataset used in this study. This hybrid univariate model is compared with the single ARIMA, single LSTM, and existing traffic prediction models using MAE, MSE, RMSE, and MAPE as evaluation indicators. This model provides the lowest values of MAE, MSE, RMSE, and MAPE as 0.499, 0.337, 0.58, and 0.03, respectively. The proposed model can help to predict the vehicle count on the road, which in turn, can enhance the quality of life for citizens living in smart cities. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

► Show Figures

Figure 1

18 pages, 27646 KiB

Open AccessArticle

Prediction of Road Traffic Accidents on a Road in Portugal: A Multidisciplinary Approach Using Artificial Intelligence, Statistics, and Geographic Information Systems

by Paulo Infante, Gonçalo Jacinto, Daniel Santos, Pedro Nogueira, Anabela Afonso, Paulo Quaresma, Marcelo Silva, Vitor Nogueira, Leonor Rego, José Saias, Patrícia Góis and Paulo R. Manuel

Information 2023, 14(4), 238; https://doi.org/10.3390/info14040238 - 13 Apr 2023

Viewed by 2107

Abstract

Road Traffic Accidents (RTA) cause human losses and irreparable physical and psychological damage to many of the victims. They also involve a very relevant economic dimension. It is urgent to improve the management of human and material resources for more effective prevention. This [...] Read more.

Road Traffic Accidents (RTA) cause human losses and irreparable physical and psychological damage to many of the victims. They also involve a very relevant economic dimension. It is urgent to improve the management of human and material resources for more effective prevention. This work makes an important contribution by presenting a methodology that allowed for achieving a predictive model for the occurrence of RTA on a road with a high RTA rate. The prediction is obtained for each road segment for a given time and day and combines results from statistical methods, spatial analysis, and artificial intelligence models. The performance of three Machine Learning (ML) models (Random Forest, C5.0 and Logistic Regression) is compared using different approaches for imbalanced data (random sampling, directional sampling, and Random Over-Sampling Examples (ROSE)) and using different segment lengths (500 m and 2000 m). This study used RTA data from 2016–2019 (training) and from May 2021–June 2022 (test). The most effective model was an ML logistic regression with the ROSE approach, using segments length 500 m (sensitivity = 87%, specificity = 60%, AUC = 0.82). The model was implemented in a digital application, and a Portuguese security force is already using it. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

► Show Figures

Figure 1

15 pages, 2668 KiB

Open AccessArticle

Mixed Scheduling Model for Limited-Stop and Normal Bus Service with Fleet Size Constraint

by Xiaohong Jiang and Jianxiao Ma

Information 2021, 12(10), 400; https://doi.org/10.3390/info12100400 - 28 Sep 2021

Cited by 1 | Viewed by 1728

Abstract

Limited-stop service is useful to increase operation efficiency where the demand is unbalanced at different stops and unidirectional. A mixed scheduling model for limited-stop buses and normal buses is proposed considering the fleet size constraint. This model can optimize the total cost in [...] Read more.

Limited-stop service is useful to increase operation efficiency where the demand is unbalanced at different stops and unidirectional. A mixed scheduling model for limited-stop buses and normal buses is proposed considering the fleet size constraint. This model can optimize the total cost in terms of waiting time, in-vehicle time and operation cost by simultaneously adjusting the frequencies of limited-stop buses and normal buses. The feasibility and validity of the proposed model is shown by applying it to one bus route in the city of Zhenjiang, China. The results indicate that the mixed scheduling service can reduce the total cost and travel time compared with the single scheduling service in the case of unbalanced passenger flow distribution and fleet constraints. With a larger fleet, the mixed scheduling service is superior. There is an optimal fleet allocation that minimizes the cost for the system, and a significant saving could be attained by the mixed scheduling service. This study contributed to the depth analysis of the relationship among the influencing factors of mixed scheduling, such as fleet size constraint, departure interval and cost. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

► Show Figures

Figure 1

15 pages, 38546 KiB

Open AccessArticle

The Expectations of the Residents of Szczecin in the Field of Telematics Solutions after the Launch of the Szczecin Metropolitan Railway

by Agnieszka Barczak

Information 2021, 12(8), 339; https://doi.org/10.3390/info12080339 - 23 Aug 2021

Cited by 5 | Viewed by 1640

Abstract

Transport is integral to every city, having a crucial impact on its functioning and development. As road infrastructure does not keep up to speed with the constantly growing numbers of vehicles on roads, new solutions are required. Fast urban railway systems are a [...] Read more.

Transport is integral to every city, having a crucial impact on its functioning and development. As road infrastructure does not keep up to speed with the constantly growing numbers of vehicles on roads, new solutions are required. Fast urban railway systems are a solution that can reduce transport congestion, with environmental protection issues also taken into account. Contemporary public transport cannot function without modern communication and information technologies. The use of telematics in public transport allows passenger mobility to be sustainable and efficient. Therefore, it seems justified to conduct research on this issue. The aim of the study is to analyze the perception of the use of telematics solutions to service SKM in Szczecin (Poland) with the use of multivariate correspondence analysis. Results of the research indicate that people living in the area of gravity of the SKM have a positive opinion on the application of telematics solutions in the activities of the Szczecin Metropolitan Railway. The results obtained are local in nature, but show the direction that researchers can take in analyzing public transport in other agglomerations. In addition, the article presents a tool that greatly facilitates the analysis of survey data, even with a large number of results. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

► Show Figures

Figure 1

17 pages, 4470 KiB

Open AccessArticle

Dynamic Optimal Travel Strategies in Intelligent Stochastic Transit Networks

by Agostino Nuzzolo and Antonio Comi

Information 2021, 12(7), 281; https://doi.org/10.3390/info12070281 - 13 Jul 2021

Cited by 15 | Viewed by 2269

Abstract

This paper addresses the search for a run-based dynamic optimal travel strategy, to be supplied through mobile devices (apps) to travelers on a stochastic multiservice transit network, which includes a system forecasting of bus travel times and bus arrival times at stops. The [...] Read more.

This paper addresses the search for a run-based dynamic optimal travel strategy, to be supplied through mobile devices (apps) to travelers on a stochastic multiservice transit network, which includes a system forecasting of bus travel times and bus arrival times at stops. The run-based optimal strategy is obtained as a heuristic solution to a Markovian decision problem. The hallmarks of this paper are the proposals to use only traveler state spaces and estimates of dispersion of forecast bus arrival times at stops in order to determine transition probabilities. The first part of the paper analyses some existing line-based and run-based optimal strategy search methods. In the second part, some aspects of dynamic transition probability computation in intelligent transit systems are presented, and a new method for dynamic run-based optimal strategy search is proposed and applied. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

► Show Figures

Figure 1

10 pages, 733 KiB

Open AccessArticle

Optimization Model for the Supply Volume of Bike-Sharing: Case Study in Nanjing, China

by Jiajie Yu, Yanjie Ji, Chenyu Yi, Chenchen Kuai and Dmitry Ivanovich Samal

Information 2021, 12(5), 182; https://doi.org/10.3390/info12050182 - 23 Apr 2021

Cited by 3 | Viewed by 2334

Abstract

In order to solve the oversupply and repositioning problems of bike-sharing, this paper proposes an optimization model to obtain a reasonable supply volume scheme for bike-sharing and infrastructure configuration planning. The optimization model is constrained by the demand for bike-sharing, urban traffic carrying [...] Read more.

In order to solve the oversupply and repositioning problems of bike-sharing, this paper proposes an optimization model to obtain a reasonable supply volume scheme for bike-sharing and infrastructure configuration planning. The optimization model is constrained by the demand for bike-sharing, urban traffic carrying capacity (road network and parking facilities carrying capacities), and the flow conservation of shared bikes in each traffic analysis zone. The model was formulated through mixed-integer programming with the aim of minimizing the total costs for users and bike-sharing enterprises (including the travel cost of users, production and maintenance costs of shared bikes, and repositioning costs). CPLEX was used to obtain the optimal solution for the model. Then, the optimization model was applied to 183 traffic analysis zones in Nanjing, China. The results showed that not only were user demands met, but the load ratios of the road network and parking facilities with respect to bike-sharing in each traffic zone were all decreased to lower than 1.0 after the optimization, which established the rationality and effectiveness of the optimization results. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

► Show Figures