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Safety studies have indicated strong correlation between road friction and accident risk, with a dramatic increase in accident when friction drops below certain threshold. For this reason, managing pavement skid resistance is an important mean to reduce crashes. Unfortunately, during the pavement lifespan, skid resistance undergoes to deterioration due to several factors (traffic wear, weathering and aging). The correct management of road pavements implies the knowledge of the performance evolution, obtained both with monitoring and degradation models, however, among those latter available in literature, very few explored the influence of traffic vehicles in terms of type and travel mode. In this paper a new methodology combining the use of road sectioning schemes with a traffic damage criterion based on the dissipated energy at the tire-road pavement contact, for the development of degradation curves from experimental data collected on roads with different traffic, in terms of volumes, vehicle composition and motion conditions, is presented. The methodology has been validated to an open graded bituminous surface course (OGSC) on urban motorway and obtained degradation models have been also compared with those provided by a traditional degradation modelling approach highlighting the superior performance of the proposed approach.

Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Hence, Intelligent Transportation Systems are often employed to enhance the Level of Service (LoS). One such approach is Variable Speed Limit (VSL) control. VSL increases the LoS and safety on motorways by optimizing the speed limit according to the traffic conditions. The proliferation of Connected and Autonomous Vehicles (CAVs) presents fresh prospects for improving the operation and measurement of traffic states for the operation of the VSL control system. This paper introduces a method for the detection of multiple congested areas that is used for state estimation for a dynamically positioned VSL control system for urban motorways. The method utilizes Q-Learning (QL) and CAVs as mobile sensors and actuators. The proposed control approach, named Congestion Detection QL Dynamic Position VSL (CD-QL-DPVSL), dynamically detects all of the congested areas and applies two sets of actions, involving the dynamic positioning of speed limit zones and imposed speed limits for all detected congested areas simultaneously. The proposed CD-QL-DPVSL control approach underwent an evaluation across six distinct traffic scenarios, encompassing CAV penetration rates spanning from 10 % to 100 % and demonstrated a significantly better performance compared to other control approaches, including no control, rule-based VSL, two Speed-Transition-Matrices-based QL-VSL configurations with fixed speed limit zone positions, and a Speed-Transition-Matrices-based QL-DVSL with a dynamic speed limit zone position. It achieved enhancements in macroscopic traffic parameters such as the Mean Travel Time and Total Time Spent by adapting its control policy to every simulated scenario. [ABSTRACT FROM AUTHOR]

Current transport infrastructure and traffic management systems are overburdened due to the increasing demand for road capacity, which often leads to congestion. Building more infrastructure is not always a practical strategy to increase road capacity. Therefore, services from Intelligent Transportation Systems (ITSs) are commonly applied to increase the level of service. The growth of connected and autonomous vehicles (CAVs) brings new opportunities to the traffic management system. One of those approaches is Variable Speed Limit (VSL) control, and in this paper a VSL based on Q-Learning (QL) using CAVs as mobile sensors and actuators in combination with Speed Transition Matrices (STMs) for state estimation is developed and examined. The proposed Dynamic STM-QL-VSL (STM-QL-DVSL) algorithm was evaluated in seven traffic scenarios with CAV penetration rates ranging from 10 % to 100 % . The proposed STM-QL-DVSL algorithm utilizes two sets of actions that include dynamic speed limit zone positions and computed speed limits. The proposed algorithm was compared to no control, rule-based VSL, and two STM-QL-VSL configurations with fixed VSL zones. The developed STM-QL-DVSL outperformed all other control strategies and improved measured macroscopic traffic parameters like Total Time Spent ( T T S ) and Mean Travel Time ( M T T ) by learning the control policy for each simulated scenario. [ABSTRACT FROM AUTHOR]

Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Hence, Intelligent Transportation Systems are often employed to enhance the Level of Service (LoS). One such approach is Variable Speed Limit (VSL) control. VSL increases the LoS and safety on motorways by optimizing the speed limit according to the traffic conditions. The proliferation of Connected and Autonomous Vehicles (CAVs) presents fresh prospects for improving the operation and measurement of traffic states for the operation of the VSL control system. This paper introduces a method for the detection of multiple congested areas that is used for state estimation for a dynamically positioned VSL control system for urban motorways. The method utilizes Q-Learning (QL) and CAVs as mobile sensors and actuators. The proposed control approach, named Congestion Detection QL Dynamic Position VSL (CD-QL-DPVSL), dynamically detects all of the congested areas and applies two sets of actions, involving the dynamic positioning of speed limit zones and imposed speed limits for all detected congested areas simultaneously. The proposed CD-QL-DPVSL control approach underwent an evaluation across six distinct traffic scenarios, encompassing CAV penetration rates spanning from 10% to 100% and demonstrated a significantly better performance compared to other control approaches, including no control, rule-based VSL, two Speed-Transition-Matrices-based QL-VSL configurations with fixed speed limit zone positions, and a Speed-Transition-Matrices-based QL-DVSL with a dynamic speed limit zone position. It achieved enhancements in macroscopic traffic parameters such as the Mean Travel Time and Total Time Spent by adapting its control policy to every simulated scenario.

Current transport infrastructure and traffic management systems are overburdened due to the increasing demand for road capacity, which often leads to congestion. Building more infrastructure is not always a practical strategy to increase road capacity. Therefore, services from Intelligent Transportation Systems (ITSs) are commonly applied to increase the level of service. The growth of connected and autonomous vehicles (CAVs) brings new opportunities to the traffic management system. One of those approaches is Variable Speed Limit (VSL) control, and in this paper a VSL based on Q-Learning (QL) using CAVs as mobile sensors and actuators in combination with Speed Transition Matrices (STMs) for state estimation is developed and examined. The proposed Dynamic STM-QL-VSL (STM-QL-DVSL) algorithm was evaluated in seven traffic scenarios with CAV penetration rates ranging from 10% to 100%. The proposed STM-QL-DVSL algorithm utilizes two sets of actions that include dynamic speed limit zone positions and computed speed limits. The proposed algorithm was compared to no control, rule-based VSL, and two STM-QL-VSL configurations with fixed VSL zones. The developed STM-QL-DVSL outperformed all other control strategies and improved measured macroscopic traffic parameters like Total Time Spent (TTS) and Mean Travel Time (MTT) by learning the control policy for each simulated scenario.

Decisions on efficient detours are crucial for motorway operations following closures. This paper presents a framework for supporting decision making on detour routing in urban motorways. A Route Efficiency Index (REI) is developed for that purpose; the index is based on three major criteria: safety, user cost and environmental impacts in the vicinity of a detour. The Athens (Greece) ring-road is used as an application setting for the proposed methodology. Results show that safety is more important for decision making compared to other criteria, as it is affected by physical characteristics of the detour rather than diverted traffic volumes. Based on the paper findings, a tool for straightforwardly deriving and assessing detour action plans for urban motorways, is developed. [ABSTRACT FROM AUTHOR]

Due to unavoidable expansion of car-ownership, traffic congestion and emissions in developing countries keep on increasing. Meanwhile, high construction costs and statutory restrictions hinder traditional road infrastructure expansion and improvement projects. Thus, exploring cost-effective traffic management solutions to relieve traffic congestion and emissions becomes one of the most significant challenges faced by transportation authorities, especially in developing countries. Active traffic management (ATM) systems that are environmentally sustainable and relatively low-cost are particularly advantageous. In this study, we develop an integrated ATM system to mitigate urban motorway congestion and emissions. The proposed system can be used directly with existing road facilities, which provides a cost-effective way for traffic management and decarbonization in developing countries. The simulation results show that the developed system can reduce travel time spent (TTS), carbon dioxide (CO 2) and nitrogen oxides (NO 2) by 7.5%, 21.1% and 10.7%, respectively. [ABSTRACT FROM AUTHOR]

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Abstract: In recent years, several studies show that people who live, work or attend school near the main roadways have an increased incidence and severity of health problems that may be related with traffic emissions of air pollutants. The concentrations of near-road atmospheric pollutants vary depending on traffic patterns, environmental conditions, topography and the presence of roadside structures. In this study, the vertical and horizontal variation of nitrogen dioxide (NO2) and benzene (C6H6) concentration along a major city ring motorway were analysed. The main goal of this study is to try to establish a distance from this urban motorway considered “safe” concerning the air pollutants human heath limit values and to study the influence of the different forcing factors of the near road air pollutants transport and dispersion. Statistic significant differences (p =0.001, Kruskal–Wallis test) were observed between sub-domains for NO2 representing different conditions of traffic emission and pollutants dispersion, but not for C6H6 (p =0.335). Results also suggest significant lower concentrations recorded at 100m away from roadway than at the roadside for all campaigns (p <0.016 (NO2) and p <0.036 (C6H6), Mann–Whitney test). In order to have a “safe” life in homes located near motorways, the outdoor concentrations of NO2 must not exceed 44–60.0μgm−3 and C6H6 must not exceed 1.4–3.3μgm−3. However, at 100m away from roadway, 81.8% of NO2 receptors exceed the annual limit value of human health protection (40μgm−3) and at the roadside this value goes up to 95.5%. These findings suggest that the safe distance to an urban motorway roadside should be more at least 100m. This distance should be further studied before being used as a reference to develop articulated urban mobility and planning policies. [Copyright &y& Elsevier]

Modern urban mobility needs new solutions to resolve high-complexity demands on urban traffic-control systems, including reducing congestion, fuel and energy consumption, and exhaust gas emissions. One example is urban motorways as key segments of the urban traffic network that do not achieve a satisfactory level of service to serve the increasing traffic demand. Another complex need arises by introducing the connected and autonomous vehicles (CAVs) and accompanying additional challenges that modern control systems must cope with. This study addresses the problem of decreasing the negative environmental aspects of traffic, which includes reducing congestion, fuel and energy consumption, and exhaust gas emissions. We applied a variable speed limit (VSL) based on Q-Learning that utilizes electric CAVs as speed-limit actuators in the control loop. The Q-Learning algorithm was combined with the two-step temporal difference target to increase the algorithm's effectiveness for learning the VSL control policy for mixed traffic flows. We analyzed two different optimization criteria: total time spent on all vehicles in the traffic network and total energy consumption. Various mixed traffic flow scenarios were addressed with varying CAV penetration rates, and the obtained results were compared with a baseline no-control scenario and a rule-based VSL. The data about vehicle-emission class and the share of gasoline and diesel human-driven vehicles were taken from the actual data from the Croatian Bureau of Statistics. The obtained results show that Q-Learning-based VSL can learn the control policy and improve the macroscopic traffic parameters and total energy consumption and can reduce exhaust gas emissions for different electric CAV penetration rates. The results are most apparent in cases with low CAV penetration rates. Additionally, the results indicate that for the analyzed traffic demand, the increase in the CAV penetration rate alleviates the need to impose VSL control on an urban motorway. [ABSTRACT FROM AUTHOR]