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Algorithms, Models and New Technologies for Sustainable Traffic Management and Safety

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 16526

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Dr. Vittorio Astarita

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Department of Civil Engineering, University of Calabria, 87036 Rende, Italy
Interests: transportation; road traffic simulation; road traffic safety; mobile computing applied to transportation systems; smart traffic lights; road safety performances
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Special Issue Information

Dear Colleagues,

New technologies such as co-operative intelligent transportation systems (C-ITS) and "connected" and "autonomous" vehicles are attracting a lot of attention, and one of the main goals of these systems is to improve sustainability and safety levels on the road networks through new services. This Special Issue intends to gather the best contributions in terms of new algorithms, models, and technologies that can present ways to improve transportation systems by making all traffic networks more sustainable in terms of reduced energy consumption, improved safety, and reduced pollution. Special attention is dedicated to road traffic and the the use of new technologies and concepts that can improve sustainability and safety, as well as papers that introduce the use of traffic conflict techniques and other simulation techniques that can assess traffic network safety and identify problems before severe crashes occur, enabling a proactive approach.

Prof. Dr. Vittorio Astarita
Guest Editor

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Keywords

traffic management
traffic simulation
intelligent transportation systems (ITS)
traffic safety
traffic sustainability

Published Papers (6 papers)

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Research

12 pages, 1463 KiB

Open AccessArticle

Calibrating the Wiedemann 99 Car-Following Model for Bicycle Traffic

by Heather Kaths, Andreas Keler and Klaus Bogenberger

Sustainability 2021, 13(6), 3487; https://0-doi-org.brum.beds.ac.uk/10.3390/su13063487 - 22 Mar 2021

Cited by 9 | Viewed by 2814

Abstract

Car-following models are used in microscopic simulation tools to calculate the longitudinal acceleration of a vehicle based on the speed and position of a leading vehicle in the same lane. Bicycle traffic is usually included in microscopic traffic simulations by adjusting and calibrating behavior models developed for motor vehicle traffic. However, very little work has been carried out to examine the following behavior of bicyclists, calibrate following models to fit this observed behavior, and determine the validity of these calibrated models. In this paper, microscopic trajectory data collected in a bicycle simulator study are used to estimate the following parameters of the psycho-physical Wiedemann 99 car-following model implemented in PTV Vissim. The Wiedemann 99 model is selected due to the larger number of assessable parameters and the greater possibility to calibrate the model to fit observed behavior. The calibrated model is validated using the indicator average queue dissipation time at a traffic light on the facilities ranging in width between 1.5 m to 2.5 m. Results show that the parameter set derived from the microscopic trajectory data creates more realistic simulated bicycle traffic than a suggested parameter set. However, it was not possible to achieve the large variation in average queue dissipation times that was observed in the field with either of the tested parameter sets. Full article

(This article belongs to the Special Issue Algorithms, Models and New Technologies for Sustainable Traffic Management and Safety)

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24 pages, 2187 KiB

Open AccessArticle

Feasibility of Stochastic Models for Evaluation of Potential Factors for Safety: A Case Study in Southern Italy

by Giuseppe Guido, Sina Shaffiee Haghshenas, Sami Shaffiee Haghshenas, Alessandro Vitale, Vittorio Astarita and Ashkan Shafiee Haghshenas

Sustainability 2020, 12(18), 7541; https://0-doi-org.brum.beds.ac.uk/10.3390/su12187541 - 12 Sep 2020

Cited by 29 | Viewed by 2107

Abstract

There is no definite conclusion about what the main variables that play a fundamental role in road safety are. Therefore, the identification of significant factors in road accidents has been a primary concern of the transportation safety research community for many years. Every accident is influenced by multiple variables that, in a given time interval, concur to cause a crash scenario. Information coming from crash reports is very useful in traffic safety research, and several reported crash variables can be analyzed with modern statistical methods to establish whether a classification or clustering of different crash variables is possible. Hence, this study aims to use stochastic techniques for evaluating the role of some variables in accidents with a clustering analysis. The variables that are considered in this paper are light conditions, weekday, average speed, annual average daily traffic, number of vehicles, and type of accident. For this purpose, a combination of particle swarm optimization (PSO) and the genetic algorithm (GA) with the k-means algorithm was used as the machine-learning technique to cluster and evaluate road safety data. According to a multiscale approach, based on a set of data from two years of crash records collected from rural and urban roads in the province of Cosenza, 154 accident cases were accurately investigated and selected for three categories of accident places, including straight, intersection, and other, in each urban and rural network. PSO had a superior performance, with 0.87% accuracy on urban and rural roads in comparison with GA, although the results of GA had an acceptable degree of accuracy. In addition, the results show that, on urban roads, social cost and type of accident had the most and least influence for all accident places, while, on rural roads, although the social cost was the most notable factor for all accident places, the type of accident had the least effect on the straight sections and curves, and the number of vehicles had the least influence at intersections. Full article

(This article belongs to the Special Issue Algorithms, Models and New Technologies for Sustainable Traffic Management and Safety)

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21 pages, 1578 KiB

Open AccessArticle

Surrogate Safety Measures from Traffic Simulation: Validation of Safety Indicators with Intersection Traffic Crash Data

by Vittorio Astarita, Ciro Caliendo, Vincenzo Pasquale Giofrè and Isidoro Russo

Sustainability 2020, 12(17), 6974; https://0-doi-org.brum.beds.ac.uk/10.3390/su12176974 - 27 Aug 2020

Cited by 19 | Viewed by 3496

Abstract

The traditional analysis of road safety is based on statistical methods that are applied to crash databases to understand the significance of geometrical and traffic features on safety, or in order to localize black spots. These classic methodologies, which are based on real crash data and have a solid background, usually do not explicitly consider the trajectories of vehicles at any given location. Moreover, they are not easily applicable for making comparisons between different traffic network designs. Surrogate safety measures, instead, may enable researchers and practitioners to overcome these limitations. Unfortunately, the most commonly used surrogate safety measures also present certain limits: Many of them do not take into account the severity of a potential collision and the dangers posed by road-side objects and/or the possibility of drivers being involved in a single-vehicle crash. This paper proposes a new surrogate safety indicator founded on vehicle trajectories, capable also of considering road-side objects. The validity of the proposed indicator is assessed by means of comparison between the calculation of surrogate safety measures on micro-simulated trajectories and the real crash risk obtained with data on real crashes observed at several urban intersection scenarios. The proposed experimental framework is also applied (for comparison) to classical indicators such as TTC (time to collision) and PET (post-encroachment time). Full article

(This article belongs to the Special Issue Algorithms, Models and New Technologies for Sustainable Traffic Management and Safety)

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19 pages, 2809 KiB

Open AccessArticle

Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm

by Giuseppe Guido, Sina Shaffiee Haghshenas, Sami Shaffiee Haghshenas, Alessandro Vitale, Vincenzo Gallelli and Vittorio Astarita

Sustainability 2020, 12(17), 6735; https://0-doi-org.brum.beds.ac.uk/10.3390/su12176735 - 20 Aug 2020

Cited by 32 | Viewed by 2831

Abstract

Evaluating road safety is an enduring research topic in Infrastructure and Transportation Engineering. The prediction of crash risk is very important for avoiding other crashes and safeguarding road users. According to this task, awareness of the number of vehicles involved in an accident contributes greatly to safety analysis, hence, it is necessary to predict it. In this study, the main aim is to develop a binary model for predicting the number of vehicles involved in an accident using Neural Networks and the Group Method of Data Handling (GMDH). For this purpose, 775 accident cases were accurately recorded and evaluated from the urban and rural areas of Cosenza in southern Italy and some notable parameters were considered as input data including Daylight, Weekday, Type of accident, Location, Speed limit and Average speed; and the number of vehicles involved in an accident was considered as output. In this study, 581 cases were selected randomly from the dataset to train and the rest were used to test the developed binary model. A confusion matrix and a Receiver Operating Characteristic curve were used to investigate the performance of the proposed model. According to the obtained results, the accuracy values of the prediction model were 83.5% and 85.7% for testing and training, respectively. Finally, it can be concluded that the developed binary model can be applied as a reliable tool for predicting the number of vehicles involved in an accident. Full article

(This article belongs to the Special Issue Algorithms, Models and New Technologies for Sustainable Traffic Management and Safety)

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15 pages, 272 KiB

Open AccessArticle

Investigation of Freight Agents’ Interaction Considering Partner Selection and Joint Decision Making

by Dapeng Zhang and Xiaokun (Cara) Wang

Sustainability 2020, 12(9), 3636; https://0-doi-org.brum.beds.ac.uk/10.3390/su12093636 - 01 May 2020

Cited by 1 | Viewed by 1568

Abstract

Freight transportation plays an increasingly important role in sustainable development. However, freight travel demand has not been understood comprehensively, due to its unique features: freight activities are the result of collaboration among freight agents. It distinguishes freight transportation from passenger transportation, in which travel decisions are made mostly by individuals. Specifically, two processes in the collaboration can be observed: partner selection and joint decision making. Using the supplier-customer collaboration as an example, partner selection is a process for suppliers and customers to evaluate their potential partners and select the best one. Joint decision making allows suppliers and customers to seek common interests and make compromises. As a traditional travel demand model cannot model the two processes effectively, this research develops an innovative econometric model, spatial matching model, to bridge the gap. The proposed model is specified based on freight agents’ behavioral, estimated by Bayesian MCMC methods, and demonstrated by numerical examples. The proposed model and estimation methods can recover the coefficient values in the econometric models, and establish the relationship between the influential factors and the observed matching behavior. The analysis improves the understanding of freight travel demand in a behavioral-consistent manner and enriches the body of freight demand modeling literature. Full article

(This article belongs to the Special Issue Algorithms, Models and New Technologies for Sustainable Traffic Management and Safety)

22 pages, 5498 KiB

Open AccessArticle

Validation of Simulated Safety Indicators with Traffic Crash Data

by Borja Alonso, Vittorio Astarita, Luigi Dell’Olio, Vincenzo Pasquale Giofrè, Giuseppe Guido, Marcella Marino, William Sommario and Alessandro Vitale

Sustainability 2020, 12(3), 925; https://doi.org/10.3390/su12030925 - 27 Jan 2020

Cited by 7 | Viewed by 2797

Abstract

The purpose of this document is to validate a new methodology useful for the estimation of road accidents resulting from possible driver distractions. This was possible through a statistical comparison made between real accident data between 2016 and 2018 in the city of Santander (Spain) and simulated data resulting from the application of the methodology on two areas of study. The methodology allows us to evaluate possible collisions starting from the knowledge of vehicular trajectories extrapolated from microsimulation. Studies show that there are good correlations between the real data and the simulated data. The results obtained show that the proposed methodology can be considered reliable and, therefore, it could be of fundamental importance for designers, since it would simplify the choice between different possible intervention scenarios, determining which is the least risky in terms of road safety. Full article

(This article belongs to the Special Issue Algorithms, Models and New Technologies for Sustainable Traffic Management and Safety)

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