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In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

As the problems with managing traffic in cities get worse, this paper looks at a way to make it easier to judge safety and handle risks in the flow of traffic on major roads in cities. By combining artificial driving and smart network connection, the study aims to find better ways to predict, lower, and respond to possible risks on city roads. Getting a lot of data from various sources, such as traffic cameras, sensors, and systems that link vehicles, is part of the research method. Artificial driving models are used to simulate and guess how traffic will move. Cars and buildings can share data in real time through an intelligent network connection. This paper carefully looks at safety by measuring things like collision rates and response times, and then it compares the findings to what happened when traditional traffic management methods were used. Adaptive traffic control and predictive analytics are two types of risk management that are talked about as ways to stop safety problems before they happen. Case studies show examples of how artificial driving and smart network connections have been used successfully to improve road safety in cities. At the end of the paper, issues that came up are discussed, and ideas are given for future research that will help improve how safety is assessed and risks are managed in metro areas where arterial traffic flow is changing. [ABSTRACT FROM AUTHOR]

Economic development has led to a continuous increase in people's demand for transportation, which has led to an imbalance between the supply and demand of road facilities. Traffic congestion, traffic safety and other issues have caused the road traffic environment to become increasingly poor, thereby hindering the development of cities. Therefore, in response to the inability of traditional traffic flow prediction techniques to handle constantly changing traffic flow data, a machine learning traffic flowing predicting model on the foundation of least squares support vector machine is proposed. A hybrid optimizing algorithm based on particle swarm optimization and genetic algorithm is proposed to optimize the parameters of least squares support vector machines in response to the sensitivity of model parameters. Three factors of genetic algorithm are introduced into particle swarm optimization to optimize this model. These experiments confirm that the fitting accuracy between model's predicted values and the actual values is over 90%, and the residual fluctuation is relatively small. Its RMSE is approximately 33.52% to 75.76% lower than the other three algorithms, its MAE is approximately 27% to 67% lower than other algorithms, and its EC is approximately 1% to 5% higher than other algorithms. The proposed hybrid optimization algorithm can find a set of optimal parameters, making the least squares support vector machine have good stability and prediction accuracy in predicting traffic flow problems. This model can be used to predict traffic flow, congestion conditions and other traffic indicators, providing a reliable theoretical basis for urban traffic management. [ABSTRACT FROM AUTHOR]

Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts. [ABSTRACT FROM AUTHOR]

Efficient traffic management has become a major concern within the framework of smart city projects. However, the increasing complexity of data exchanges and the growing importance of big data makes this task more challenging. Vehicular ad hoc networks (VANETs) face various challenges, including the management of massive data generated by different entities in their environment. In this context, a proposal is put forth for a real-time anomaly detection system with parallel data processing, thereby speeding up data processing. This approach accurately computes vehicle density for each section at any given time, enabling precise traffic management and the provision of information to vehicles regarding traffic density and the safest route to their destination. Furthermore, a machine learning-based prediction system has been developed to mitigate congestion problems and reduce accident risks. Simulations demonstrate that the proposed solution effectively addresses transportation issues while maintaining low latency and high precision.

Operating autonomous vehicles (AVs) and human-driven vehicles (HVs) at urban intersections while observing requirements of safety and service level is complex due not only to the existence of multiple inflow and outflow lanes, conflicting crossing zones, and low-speed conditions but also due to differences between control mechanisms of HVs and AVs. Intelligent intersection management (IIM) strategies can tackle the coordination of mixed AV/HV intersections while improving intersection throughput and reducing travel delays and fuel wastage in the average case. An endeavor relevant to traffic planning and safety is assessing whether given worst-case service levels can be met. Given a specific arrival pattern, this can be done via the worst-case response time (WCRT) that any vehicle experiences when crossing intersections. In this research line, this paper estimates WCRT upper bounds and discusses the analytical characterization of arrival and service curves, including estimating maximum queue length and associated worst-case waiting time for various traffic arrival patterns. This analysis is then used to compare six state-of-the-art intersection management approaches from conventional to intelligent and synchronous. The analytical results show the advantage of employing a synchronous management approach and are validated with the vehicles floating car data (timestamped location and speed) and simulations carried out using SUMO.

The research methodology involves the collection of data using CCTV cameras, and the Google Maps API to analyze traffic patterns and density at intersections. The system dynamically adjusts traffic light timings based on real-time traffic conditions, harnessing the power of the Google API for enhanced accuracy and efficiency. The ATLMS (Adaptive Traffic Light Management System) demonstrates significant improvements in traffic management by reducing waiting times, enhancing traffic flow, and minimizing delays at intersections. Notably, the system considers traffic density as a critical factor in traffic light timing adjustments, leading to optimized traffic movement. Additionally, the intelligent components facilitate efficient emergency vehicle prioritization and improve pedestrian safety through the implementation of smart crosswalk systems. The findings underscore the significance of ATLMS which integrate traffic density analysis, the Google API, emergency vehicle management, and pedestrian safety measures. The ATLMS offers a scalable solution for various intersection types and configurations, effectively addressing the unique traffic challenges of urban areas. By considering traffic density, incorporating accurate data from the Google API, prioritizing emergency vehicles, and enhancing pedestrian safety, the system provides a comprehensive solution for efficient traffic management. Future research endeavors may explore further enhancements and the integration of advanced technologies to meet the evolving traffic demands of urban areas. [ABSTRACT FROM AUTHOR]

Major arterial roads in city centres often face ever-increasing traffic demand periodically addressed through developments to ease congestion on road links and improve integrated road connectivity. Towards this end, three flyovers are proposed in the Slave Island area in Colombo. This high-investment project of the Sri Lankan Government expects the flyovers to prevent peak-hour traffic delays from the frequent road closures at two railway gates while providing for the higher traffic generated from the land use changes in the area. The project also expects to relieve congestion in the adjacent road network. This research investigates the outcomes expected of integrated traffic management from the project. A traffic analysis was carried out for the entire road network of the project impact area using the PTV Vissim software. Moreover, current and future land use was considered in assessing network connectivity over the design life. Alternative traffic management options were tested to identify the most feasible arrangement for connecting the proposed flyovers. Origin-Destination data from 2018 were used to determine intersection capacities and pedestrian movements. Results reveal that delays due to railway gate closure reduced, though a significant traffic diversion from the proposed flyover network was not evident.

Populational growth increases the number of cars and makes the transport infrastructure increasingly saturated. The control of traffic lights by intelligent software is a promising way to solve the problem caused by this situation. This article addresses this problem that occurs in urban traffic. An agent-based simulation of an urban traffic control system is proposed. The solution is offered as intelligent traffic lights as agents to alleviate traffic congestion at a given location. Each agent controls a crossing and maintains communication with agents from other corners. Thus, they can have greater control of a larger area and identify patterns that can help them to solve congestion problems. The results of our simulated experiments point to the improvement of the urban traffic when using the proposed Multiagent System, in comparison with an approach that uses crossing agents without communication and other that implements static traffic lights.

Green Supply Chain Management (GrSCM) has become one of the most crucial innovation in the Supply Chain Management (SCM). This approach involves environmental concerns and issues into the SCM, thus, companies and authorities tend to exploit the GrSCM through logistics process in order to improve their performance. In this paper, we will give a demonstration of the added value of the Urban Traffic Management (UTM) and its integration in the concept of GrSCM, we also aim to study its impact on the performance improvement in Transport Management with a focus on Air quality improvement. This study proposes a new approach and model based on Deep learning for Urban Traffic Control Management to solve the traffic flow problem in order to reduce the congestion, improve the air quality and enhance the urban supply chain. Our proposed framework for Data collection and processing is mainly based on Internet of Thing (IoT) technologies for an efficient Smart City. [ABSTRACT FROM AUTHOR]

Urban traffic management and traffic signal control systems, denoted as Urban Traffic Control (UTC) systems, are used extensively worldwide by Local Government Authorities (LGAs) when implementing strategic transport policies. However, it is not clear how well the requirements imposed by LGA policy implementation will be met by UTC systems being developed for the future. Therefore, research was undertaken to analyse how delivery of urban transport policies over the next 5–10 years would shape LGAs' requirements for the next generation of UTC systems, and thereby to identify Key Performance Indicators (KPIs) to provide user-led guidance for future system development. A two-stage survey of LGA policy makers and implementers from around the world was conducted. The results produced consensus among the group of participants (n = 16) on 17 KPIs, representing a synthesis of expert opinions on the desired features of future UTC systems from a policy implementation perspective. The research makes an important contribution in eliciting the wide-ranging breadth of issues associated with delivering strategic transport policies and understanding how these issues affect the requirements LGAs have for future UTC systems. Aligning future UTC system capabilities with LGAs' requirements will enable more effective implementation of strategic urban transport policies worldwide and allow the benefits to society associated with those policies to be realised. • Local Government Authorities (LGAs) implement strategic transport policies. • Policy implementation is achieved using urban traffic management systems. • LGA policy makers and implementers were interviewed about desired system features. • Consensus was achieved on Key Performance Indicators (KPIs) for future systems. • The KPIs provide user-led guidance to align system capabilities with LGA policies. [ABSTRACT FROM AUTHOR]

Intelligent traffic management is an important issue for smart cities. City councils try to implement the newest techniques and performant technologies in order to avoid traffic congestion, to optimize the use of traffic lights, to efficiently use car parking, etc. To find the best solution to this problem, Birmingham City Council decided to allow open-source predictive traffic forecasting by making the real-time datasets available. This paper proposes a multi-agent system (MAS) approach for intelligent urban traffic management in Birmingham using forecasting and classification techniques. The designed agents have the following tasks: forecast the occupancy rates for traffic flow, road junctions and car parking; classify the faults; control and monitor the entire process. The experimental results show that k-nearest neighbor forecasts with high accuracy rates for the traffic data and decision trees build the most accurate model for classifying the faults for their detection and repair in the shortest possible time. The whole learning process is coordinated by a monitoring agent in order to automate Birmingham city’s traffic management.

The performance and complicated interactions of different classes of travelers on regional urban networks are presented and analyzed. A new multi-class extension of a regional dynamic traffic model, the Network Transmission Model is proposed. The classes in question correspond to travelers using autonomous vehicles, conventional vehicles, equipped with Route Guidance and Information Systems, and unequipped vehicles. Each class is represented by a different routing method. Incremental Route Planning, an innovative predictive simulation-based routing method, Proxy Regret Matching, a non-predictive strategic learning-based method and Multinomial Logit-based Routing for 1st, 2nd and 3rd class respectively. All routing methods include a Public Transit Diversion mechanism and are assumed to provide prescriptive route guidance, with pre-trip information dissemination for every departing vehicle. We consider the possibility of non-compliance for conventional vehicles equipped with Route Guidance and Information Systems. We also consider 2 possible scenarios for autonomous vehicles that affect their travel time prediction accuracy. We simulate regional traffic dynamics for simultaneous application of all aforementioned routing methods, employing a market penetration scheme for each class of travelers. We analyze results regarding the overall network performance for various combinations of traveler class market penetration rates and non-compliance rates. We come to the conclusion that autonomous vehicles will not only provide benefits for 1st class travelers, but for all traveler classes on the network. [ABSTRACT FROM AUTHOR]

In this paper we propose a solution to realize a mobile system for traffic management by counting the vehicles on a road with one or two lanes. The traffic intensity is measured using the transducers based on lidar principle. The acquired data are local stored into a local data logger and simultaneous are transmitted via radio communication to a dedicated server. The novelty of this system is represented by the communication facility provided by the new LoRa technology. System performance is evaluated by field tests and simulations. Based on the analyzes and evaluations, some solutions for monitoring and controlling processes are proposed using this communications infrastructure. Also, it is highlights that this system is mobile and it is not invasive on the road structure. [ABSTRACT FROM AUTHOR]

In the last decade, crowdsourcing has emerged as a novel mechanism for accomplishing temporal and spatial critical tasks in transportation with the collective intelligence of individuals and organizations. This paper presents a timely literature review of crowdsourcing and its applications in intelligent transportation systems (ITS). We investigate the ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and identify the problems and challenges that need further research. Finally, we briefly introduce our future works focusing on using geospatial tagged data to analyze real-time traffic conditions and the management of traffic flow in urban environment. This review aims to help ITS practitioners and researchers build a state-of-the-art understanding of crowdsourcing in ITS, as well as to call for more research on the application of crowdsourcing in transportation systems. [ABSTRACT FROM PUBLISHER]

To support an efficient carpooling service in heavy urban traffic, we propose an intelligent routing scheme based on mining Global Position System trajectories from shared riders. The carpooling system provides many-to-many services with multiple pickup and dropping points. To join a daily carpooling group, the riders must accept a compromised route that is efficient after merging the routes that are preferred by all qualified riders. We developed three frequency-correlated algorithms for route mining, rider selection, and route merging in an urban carpool service. Our approach can cope with the traffic dynamics to yield a suboptimal shared route. Our scheme was successfully tested under heavy Beijing traffic over hundreds of riders. We developed performance metrics to measure the service cost and mileage saved. The ultimate goal is to minimize the riding distances and the transportation costs, and thus alleviate urban traffic jams. [ABSTRACT FROM PUBLISHER]

Abstract: In an effort to alleviate traffic congestion and increase the efficiency of public transport in Dublin’s city centre, a ‘bus gate’ was introduced to one particularly sensitive area in the city centre. The scheme restricts private vehicles from accessing the area during peak traffic hours with the aim of generating significant journey time-savings for public transport users and reduced noise pollution in the city centre. This paper quantifies the effect the ‘bus gate’ has had on noise levels in the area. Noise levels were monitored prior to and after the introduction of the scheme and the extent to which the scheme impacted on the noise levels was thus evaluated. The study also estimates the impact extending the ban would have on noise exposure levels in Dublin city centre. [Copyright &y& Elsevier]

Abstract: This paper examines an environmental-friendly parking policy to reduce vehicle-mile traveled, and in consequence results in lower greenhouse gas emissions. Focusing on chained trips that have become an appealing travel type, but have received comparatively less attention, this paper studies on these behaviors for setting an environmental-friendly parking fee. The equilibrium number of travelers for each behavior is obtained based on a given parking fee. In addition, some parking policies are analyzed and a minimum parking fee to cut vehicle mileage is suggested. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on cooperative advanced sensors for road traffic applications, real data have been collected on an urban intersection equipped with two cameras. The results obtained show that the data fusion process enhances the quality of occupancy patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available. [Copyright &y& Elsevier]

Intelligent traffic management is an important issue for smart cities. City councils try to implement the newest techniques and performant technologies in order to avoid traffic congestion, to optimize the use of traffic lights, to efficiently use car parking, etc. To find the best solution to this problem, Birmingham City Council decided to allow open-source predictive traffic forecasting by making the real-time datasets available. This paper proposes a multi-agent system (MAS) approach for intelligent urban traffic management in Birmingham using forecasting and classification techniques. The designed agents have the following tasks: forecast the occupancy rates for traffic flow, road junctions and car parking; classify the faults; control and monitor the entire process. The experimental results show that k-nearest neighbor forecasts with high accuracy rates for the traffic data and decision trees build the most accurate model for classifying the faults for their detection and repair in the shortest possible time. The whole learning process is coordinated by a monitoring agent in order to automate Birmingham city's traffic management. [ABSTRACT FROM AUTHOR]

Intelligent traffic management is an important issue for smart cities. City councils try to implement the newest techniques and performant technologies in order to avoid traffic congestion, to optimize the use of traffic lights, to efficiently use car parking, etc. To find the best solution to this problem, Birmingham City Council decided to allow open-source predictive traffic forecasting by making the real-time datasets available. This paper proposes a multi-agent system (MAS) approach for intelligent urban traffic management in Birmingham using forecasting and classification techniques. The designed agents have the following tasks: forecast the occupancy rates for traffic flow, road junctions and car parking; classify the faults; control and monitor the entire process. The experimental results show that k-nearest neighbor forecasts with high accuracy rates for the traffic data and decision trees build the most accurate model for classifying the faults for their detection and repair in the shortest possible time. The whole learning process is coordinated by a monitoring agent in order to automate Birmingham city's traffic management.