Modelling mixed traffic flow of autonomous vehicles and human-driven vehicles

Autonomous Vehicles (AVs) are bringing revolutionary opportunities and challenges to urban transport systems. They can reduce congestion, improve operational efficiency and liberate drivers from driving. Though AVs might bring attractive potential benefits, most benefits are evaluated at high AV penetration rates or an all-AV scenario. In practice, limited by price barriers, adoption rates and vehicle-renewal periods, AVs may not replace Human-Driven Vehicles (HDVs) to achieve a high penetration rate in a short time. It can be expected that the road network will operate with a mix of AVs and HDVs in the near to medium future. Therefore, there is a strong motivation to analyse the performance of road networks under mixed traffic conditions. The overall aim of this PhD research is to analyse mixed traffic flows of AVs and HDVs to help traffic managers and Local Authorities (LAs) improve the performance of urban traffic systems by right-of-way reallocation and dynamic traffic management. To achieve this aim, this PhD research is divided into four parts. Firstly, the impact of heterogeneity between AVs and HDVs on road capacity is investigated. A theoretical model is proposed to calculate the maximum capacity of heterogeneous traffic flow. Based on the theoretical model, it is shown that road capacity increases convexly with AV penetration rates. This finding provides a theoretical basis to support the hypothesis that right-of-way reallocation can increase road capacity under the mixed traffic flow. To cross-validate the above finding, different right-of-way reallocation strategies are evaluated on a two-lane road with SUMO simulation. Compared with a do-nothing scenario, the road capacity can be increased by approximately 11% with a proper RoW reallocation strategy at low or medium AV penetration rates. Secondly, whether CAVs can be used as mobile traffic controllers by adjusting their speed on a certain link is investigated. It is found that in some circumstances, system efficiency can be improved by CAVs adjusting their speed on a certain link to nudge the network towards the system optimum. According to a numerical analysis on the Braess network, total travel time can be reduced by 9.7% when CAVs actively slow down on a link. To take more realistic circumstances into account, a SUMO simulation case study is conducted, where HDVs only have partial knowledge about travel costs. The results of the simulation demonstrate that when CAVs are acting as mobile traffic controllers by actively reducing speed on a certain link, total travel time can be reduced by approximately 6.8% compared with the do-nothing scenario. Thirdly, whether travel efficiency can be improved with only a part of the vehicular flow cooperatively changing their routing under mixed conditions is investigated. It has been found that it is possible to use CAVs to influence HDVs’ day-to-day routing and push the network towards the system optimal distribution dynamically on a large network with multiple OD pairs. Taking non-linear cost-flow relationship and signal timing into account, an Optimal Routing and Signal Timing (ORST) control strategy is proposed for CAVs and tested in simulation. Compared with initial user equilibrium, total travel time can be reduced by approximately 7% when a portion of CAVs cooperatively charge their routing with the ORST control strategy at the 75% CAV penetration rate. This opens up possibilities, besides road pricing, to improve system efficiency by controlling routing and signal timing strategy for CAVs. Fourthly, whether additional travel efficiency can be achieved by jointly optimising routing and signal timing with information from CAVs is further investigated. Specifically, the impact of information levels on routing and signal timing efficiency has been investigated quantitatively. The results demonstrate that different levels of information will lead the road traffic system to reach different equilibrium points. Then the proposed ORST control strategy is compared with existing routing and signal timing strategies. The results present that ORST can reduce approximately 10% of the total travel time compared to user equilibrium. In addition, the proposed model has also been tested on a revised Nguyen-Dupuis network. At 25% CAV penetration rates, the proposed model can successfully reduce approximately 23% of total travel time. In summary, the mixed flow of AVs and HDVs is investigated in this PhD research. To increase the efficiency of urban traffic systems, novel strategies have been proposed and tested with numerical analysis and simulation, which provides inspirations and quantitative evidence for traffic managers and LAs to manage the mixed traffic flow efficiently.Open Acces

Reduced Fuel Emissions through Connected Vehicles and Truck Platooning

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag across the convoy—could eliminate 37.9 million metric tons of CO2 emissions between 2022 and 2026

Compound Impact on Private and Public Transport Network Performance on Integration of New Forms of Mobility

Continued evolutions in autonomous drive technologies and pandemic leading to a boom in micro-mobility usage make these new forms of mobility an integral part of investigative research to assess their impacts on transportation networks. This research thesis examines their impacts in terms of: quantification of the penetration rate of autonomous vehicles (AVs), the influence of physical characteristics of the urban road network on macroscopic fundamental parameters in heterogeneous traffic stream, inequities in travel costs equilibrium, assessment of public transport (PuT) network vulnerability against random service disruptions and importance of topography for accurate provision of micro-mobility services. Some benefits for 25-35% inclusion of AVs include enhanced network capacity, improvement in travel time, decrement in travel equilibrium costs. Whereas, the integrated micro-mobility modes reduce the commuter’s dis-utility and perceived journey times by 7.14% in case of disruptions. However, the spill-over effects are to watch out for

Stability and Environmental Analysis of Mixed Traffic Flow – Using the Markov Probabilistic Theory

The rapid growth of CAV (Connected and Automated Vehicle) market penetration highlights the need to gain insight into the overall stability of mixed traffic flows in order to better deploy CAVs. Several studies have examined the modelling process and stability analysis of traffic flow in a mixed traffic environment without considering its inner spatial distribution. In this paper, an innovative Markov chain-based model is established for integrating the spatial distribution of mixed traffic flow in the model process of car-following behaviour. Then the linear stability analysis of the mixed traffic flow is conducted for different CAV market penetration rates, different CAV platoon strength and different cooperation efficiency between two continuous vehicles. Moreover, several simulations under open boundary conditions in multiple scenarios are performed to explicate how CAV market penetration rate, platoon strength and cooperation efficiency jointly influence the stability performance of the mixed traffic flow. The results reveal that the performance of this mixed traffic flow stability could be strengthened in these three factors. In addition to stability, an investigation of the fuel consumption and emission reduction under different market penetration rates and the platoon strength of CAVs are explored, suggesting that substantial potential fuel consumption and emission could be reduced under certain scenarios