Mudasser Seraj

Bio: Mudasser Seraj is an academic researcher from University of Alberta. The author has contributed to research in topics: Platoon & Bottleneck. The author has an hindex of 4, co-authored 13 publications receiving 48 citations.

Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making

Abstract: Microscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper proposes a naive microscopic car-following strategy for a mixed traffic stream in CAV settings and specified shifts in traffic mobility, safety, and environmental features. Additionally, this study explores the influences of platoon properties (i.e., intra-platoon headway, inter-platoon headway, and maximum platoon length) on traffic stream characteristics. Different combinations of MDS and ADS vehicles are simulated in order to understand the variations of improvements induced by ADS vehicles in a traffic stream. Simulation results reveal that grouping ADS vehicles at the front of traffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximum mobility benefits for upstream vehicles. Both mobility and environmental improvements can be realized by forming long, closely spaced ADS vehicles at the cost of reduced safety. To achieve balanced mobility, safety, and environmental advantages from mixed traffic environment, dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS market penetrations.

Platoon Priority Visualization Modeling and Optimization for Signal Coordination in the Connected Vehicle Environment

Abstract: Although connected vehicle (CV)-based signal coordination has some proposed prototypes and has been investigated by several different strategies, the existing works have issues that require attenti...

Implementation of Variable Speed Limits: Preliminary Test on Whitemud Drive, Edmonton, Canada

Abstract: Congestion has become highly recognized as a worldwide traffic problem, as traffic demand has grown steadily over the past few decades. Variable speed limits (VSLs) are an intelligent transportation system (ITS) measure that limits mainline flow to mitigate bottleneck congestion. Currently, VSLs have become proactive based on short-term prediction. Proactive VSLs succeed in simulation evaluations, but few have been deployed in the field and their real-world effectiveness has not been proven. Various factors may lead to this limitation, such as the absence of reliable field application software, accuracy of prediction models, and high computation time for proactive control. To address this research gap, this study reports a preliminary VSL test and details its implementation results on Whitemud Drive, Edmonton, Canada. First, based on field traffic measurements before VSL control, recurrent bottleneck locations are identified. Second, the proactive control algorithm is briefly introduced. Then, a s...

Multilane Microscopic Modeling to Measure Mobility and Safety Consequences of Mixed Traffic in Freeway Weaving Sections

Abstract: Weaving sections are components of highway networks that introduce a heightened likelihood for bottlenecks and collisions. Automated vehicle technology could address this as it holds considerable promise for transportation mobility and safety improvements. However, the implications of combining automated vehicles (AuVs) with traditional human-driven vehicles (HuVs) in weaving freeway sections have not been quantitatively measured. To address this gap, this paper objectively experimented with bidirectional (i.e., longitudinal and lateral) motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. Our research begins by establishing a multilane microscopic model for studied vehicle types (i.e., AuV and HuV) from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the Highway Capacity Manual. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share. At the same time, steadiness in the average speed of traffic was experienced with increasing AuV share. The results also revealed that a 95% AuV share could reduce potential conflicts by 94.28%. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state and the optimal AuV share for the current inflow rate in weaving sections.

The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance

Abstract: Automatic recognition of traffic signs in complex, real-world environments has become a pressing research concern with rapid improvements of smart technologies. Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automatic traffic sign recognition system that can identify widely used regulatory and warning signs in diverse driving conditions. Sign recognition performance was assessed in terms of weather and reflectivity to identify the limitations of the developed system in real-world conditions. Furthermore, we produced several editions of our sign recognition system by gradually increasing the number of training images in order to account for the significance of training resources in recognition performance. Analysis considering variable weather conditions, including fair (clear and sunny) and inclement (cloudy and snowy), demonstrated a lower susceptibility of sign recognition in the highly trained system. Analysis considering variable reflectivity conditions, including sheeting type, lighting conditions, and sign age, showed that older engineering-grade sheeting signs were more likely to go unnoticed by the developed system at night. In summary, this study incorporated automatic object detection technology to develop a novel sign recognition system to determine its real-world applicability, opportunities, and limitations for future integration with advanced driver assistance technologies.

Modeling the fundamental diagram of mixed human-driven and connected automated vehicles

Abstract: Traffic flow fundamental diagram (FD) is viewed as the basis of traffic flow theory and has various applications in transportation. However, the fundamental diagram of mixed human-driven vehicles (HVs) and connected automated vehicles (CAVs) traffic has not been well-studied. This paper derives the FD for mixed HV and CAV traffic considering the stochastic headway. Firstly, the deterministic FD of pure CAV traffic and pure HV traffic are built. Then the FD of mixed HV and CAV traffic is developed with CAV penetration and platooning intensity taken into consideration. A Gaussian mixture model (GMM) is applied to model the stochastic headway, based on which the stochastic FD is derived. Impact of CAV penetration and platooning intensity on the stochasticity of FD is studied. Results from theoretical analysis and case study show that increasing CAV penetration can reduce the scattering of FD, while higher platooning intensity may result in more scattering of FD.

Analytical analysis of the effect of maximum platoon size of connected and automated vehicles

Abstract: The maximum platoon size is a critical parameter in connected and automated vehicle (CAV) platoon configuration. However, the effect of platoon size on the transportation system has not been well-studied. This paper unveils the effect of maximum CAV platoon size in terms of road capacity and traffic flow stability. Specifically, the analytical formulations of the capacity and flow stability are developed considering the maximum platoon size. Simulations are conducted to verify the developed theoretical models. For capacity analysis, both the analytical and simulation results indicate that a larger maximum platoon size can help increase the capacity. However, the increment becomes smaller with the increase of maximum platoon size. For flow stability analysis, the theoretical analysis and microscopic simulation show that smaller maximum platoon size leads to greater traffic flow stabilization. In addition, analysis shows that improvements in capacity and traffic stability are more profound when CAV penetration and platooning intensity are high.

Optimal Control for Speed Harmonization of Automated Vehicles.

Abstract: This article addresses the problem of controlling the speed of a number of automated vehicles before they enter a speed reduction zone on a freeway. We formulate the control problem and provide an analytical, closed-form solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each vehicle under the hard safety constraint of rear-end collision avoidance. The effectiveness of the solution is evaluated through a microscopic simulation testbed and it is shown that the proposed approach reduces significantly both fuel consumption and travel time. In particular, for three different traffic volume levels, fuel consumption for each vehicle is reduced by 19-22% compared to the baseline scenario, where human-driven vehicles are considered, by 12-17% compared to the variable speed limit (VSL) algorithm, and by 18-34% compared to the vehicular-based speed harmonization (SPD-HARM) algorithm. Similarly, travel time is improved by 26-30% compared to the baseline scenario, by 3-19% compared to the VSL algorithm, and by 31-39% compared to the vehicular-based SPD-HARM algorithm.

Impacts of differentiated per-lane speed limit on lane changing behaviour: A driving simulator-based study

Abstract: Inappropriate lane changing behaviour increases a driver’s risk of being involved in a crash and the injury severity levels. It is hypothesized in this study that differentiated per-lane speed limit freeways could reduce inappropriate lane changing behaviours. A driving simulator experiment was conducted to investigate drivers’ lane changing behaviour and the corresponding workload levels when different speed limit values are assigned to different lanes. Participants (N = 36) experienced three different test scenarios with different speed limit configurations. Lane changing behaviours are compared in regards to five measures describing lane changing behaviour. Meanwhile, task workload indexes were developed to evaluate key aspects of drivers’ subjective experience of the test. The results reveal that the speed limit configurations have a considerable impact on lane changing behaviour as well as on the corresponding driving workload levels. This applies especially to the scenario with both different maximum and minimum speed limits for adjacent lanes, with drivers behaving differently than they did in other situations. Lane changes were found to be less frequent and have shorter durations, and drivers tended to avoid the dangerous behaviour of “rolling on the lane edge”, therefore these compensatory safety-prone driving strategies suggest an enhanced driving safety level in association with the differentiated per-lane speed limit scenarios. The findings could provide important references for traffic management of freeways.