Automated vehicles (AVs) are believed to have great potential to improve the traffic capacity and efficiency of the current transport systems. Despite positive findings of the impact of AVs on traffic flow and potential road capacity increase for highways, few studies have been performed regarding the impact of AVs on urban roads. Moreover, studies considering traffic volume increase with a mixture of AVs and human-driven vehicles (HDVs) have rarely been conducted. Therefore, this study investigated the impact of gradual increments of AV penetration and traffic volume on urban roads. The study adopted a microsimulation approach using VISSIM with a Wiedmann 74 model for car-following behavior. Parameters for AVs were set at the SAE level 4 of automation. A real road network was chosen for the simulation having 13 intersections in a total distance of 4.5 km. The road network had various numbers of lanes from a single lane to five lanes in one direction. The network consists of a main arterial road and a parallel road serving nearby commercial and residential blocks. In total, 36 scenarios were investigated by a combination of AV penetrations and an increase in traffic volumes. The study found that, as AV penetration increased, traffic flow also improved, with a reduction of the average delay time of up to 31%. Also, as expected, links with three or four lanes had a more significant impact on the delay. In terms of road capacity increase, when the penetration of AVs was saturated at 100%, the road network could accommodate 40% more traffic.

This research proposes a new tram progression model, TRAMBAND, which is a passive tram signal priority strategy. TRAMBAND was formulated based on the MAXBAND model, which is a traditional arterial signal optimization model to maximize bandwidth. A tram that leaves a station during an appointed green time can arrive at subsequence station without experiencing intersection delays and stops by using the tram bandwidth. In order to guarantee a desired green time for other road users and minor streets, the TRAMBAND model determines the traffic signal timings for tram passive priority using only a left-turn phase sequence and offset. This strategy also maximizes the general vehicles bandwidth in the context of the fixed tram bandwidth in a median tram rail. The tram dwell-time and its variability influence the efficiency of the passive priority. In this study, the stop time at station is divided by the dwell-time and waiting time. The tram has to wait at station during the waiting time after the dwell-time; however, the waiting time is used as slack time to absorb the dwell-time variability and to maximize the general vehicle bandwidth. The case study is based on nine signalized intersections and a micro-simulator VISSIM, wherein it demonstrates that the proposed tram priority model, TRAMBAND, is capable of computing signal timings so as to avoid intersection delays and stops of tram and maintain the general vehicle bandwidth.

In order to deal with dynamic traffic flow, adaptive traffic signal controls using reinforcement learning are being studied. However, most of the related studies are difficult to apply to the real field considering only mathematical optimization. In this study, we propose a reinforcement learning-based signal optimization model with constraints. The proposed model maintains the sequence of typical signal phases and considers the minimum green time. The model was trained using Simulation of Urban MObility (SUMO), a microscopic traffic simulator. The model was evaluated in the virtual environment similar to a real road with multiple intersections connected. The performance of the proposed model was analyzed by comparing the delay and number of stops with a reinforcement learning model that did not consider constraints and a fixed-time model. In a peak hour, the proposed model reduced the delay from 3 min 15 s to 2 min 15 s and the number of stops from 11 to 4.7 compared to the fixed-time model.

Time headway distribution modeling is fundamental to many aspects of traffic flow studies such as capacity estimation, safety analysis, and microscopic simulation. Existing time headway distribution models have focused on the behavior of general vehicles. We examine the distribution of sampled vehicle headway (e.g., probes) on both single and multiple lane highway traffic streams. This study is divided into three parts: an empirical study, a simulation analysis, and an analytical derivation. The empirical study uses probe data obtained from Houston, Texas that was collected as part of the Automatic Vehicle Identification system of the Houston Transtar system. In the empirical study, a shifted negative exponential distribution was found to give the closest fit for both single and multiple lane cases. We found that if the volume level of the probes is low, regardless of the volume level of general vehicles, the headway of the probes followed a shifted negative exponential distribution. In the simulation study, we found that the time headway of probes does not necessarily follow the time headway distribution of general vehicles. Rather, it depends on many variables such as the volume level of general vehicles, the market penetration of probe vehicles, and the number of lanes. However, when the volume level of general vehicles is low, the headway of probes tends to follow the shifted negative exponential distribution at all levels of market penetration, together with the general vehicles. We analytically proved that if the time headway of general vehicles follows the shifted negative exponential distribution, then the time headway of the probes is the same as that of the general vehicles.

Variable Speed Limit (VSL) is a traffic operation method of preventing accidents and improving traffic conditions. The VSL management technique for highway is already being applied and operated in Germany, the UK, and other countries in Europe. But there are few studies about the effect of VSL on traffic flow. This paper aims to study the principle of VSL and the effect of VSL on traffic flow theoretically using the fundamental traffic model and shockwave theory. In this paper, changes in traffic flow state with VSL are explained using the time/distance headway of the vehicle trajectory and are then converted into aggregate data in order to apply the real traffic condition easily. Finally the findings are verified by the numerical method and VISSIM simulation.

Transit signal priority (TSP) plays an essential role in reducing traffic jams in a multimodal road traffic environment. Traffic experts expect that the demand for cars will be transferred to buses as the bus-service quality improves. This study proposes a multimodal environment TSP model with dedicated median bus lanes to simultaneously increase bus travel speed at signalized intersections and reduce car delay times. The proposed TSP model includes three optimization stages. The TSP is first optimized using a MAXBAND-based bandwidth model after a partitioned TSP control group is defined relative to a median bus stop to support non-stop bus movement. The optimal combination between these groups is selected using the delay-difference-of-offset technique. A kinematic wave-based offset optimization model is proposed to reduce the car delay time. We analyze the performance of this model in improving two-way bus speeds and reducing the car delay time in Seoul’s bus rapid transit system. The proposed TSP model decreases the stop ratio of buses by analyzing the bus trajectory from the micro-simulation. This model can be used as a center-based control to improve the mobility of buses and cars in a multimodal environment.