Real-world route navigation data indicate that nontrivial portion of drivers do not prefer the system-recommended best routes. Current navigation systems have simplified assumptions about drivers’ route choice preferences and do not adequately accommodate drivers’ heterogeneous route choice preferences, mainly because of: (i) difficulty in acquiring exogenous criteria (e.g., sociodemographic information) that are typically used to differentiate drivers’ preferences in behavioral modeling; and (ii) difficulty in capturing preference of individuals due to limited preference data at the individual level. To address these, this paper introduced a human-centric machine learning technique named Multi-Task Linear Classification Model Adaption (MT-LinAdapt). It can capture drivers’ common aspects of route choice preferences and yet adapts to each driver’s own preference. In addition, any evolvement of individual drivers’ preferences can be simultaneously integrated to update the common preference for further individual drivers’ preference adaptation. This paper evaluated MT-LinAdapt against two state-of-the-art route recommendation strategies including an aggregate-level and an individual-level data-based strategies, which are categorized based on the data used for modeling. With a real-world dataset containing 30,837 drivers’ navigation usage data in Daegu City, South Korea, MT-LinAdapt was compared to existing strategies for its performance at different levels of data availability, and showed at least the same performance with existing strategies when minimum preference data is available and achieves up to 7% higher prediction accuracy as more data becomes available. Higher prediction accuracies are expected to bring better user satisfaction and compliance rates which can further help with transportation system control and management strategies.@en

Constant spacing-based platooning systems cannot guarantee string stability if platoon members only use the preceding vehicle's information. To meet string stability specification, leader-predecessor-follower (LPF) platooning systems are proposed to incorporate the information of both the preceding vehicle and the platoon leader into the control loop. However, string stability of LPF platooning systems is very sensitive to communication and sensing delays. Even a delay of 5 milliseconds may render LPF platooning systems string-unstable. This paper focuses on a new approach to deal with communication and sensing delays in LPF platooning systems. A semi-constant spacing policy that synchronizes delayed measurements of system states obtained from different sources is proposed. This spacing policy aims at tracking the past information of the preceding vehicle to gurantee string stability. Moreover, the delay-synchronizing LPF platooning system puts the same requirements on controller parameters as the nominal LPF platooning system that is not affected by communication and sensing delays. Thus, control gains of the delay-synchronizing LPF platoon can be designed without considering delays.

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In this research, an optimal control-based Cooperative Adaptive Cruise Control (CACC) system is proposed. The proposed system is able to enforce a target time gap between platoon members and is formulated in the space domain instead of the time domain which is adopted by most optimal control-based CACC systems in the past. By having this change, its robustness against communication failure is greatly improved and thus minimum safety headway buffer is reduced which leads to better mobility. In addition, third-order vehicle dynamics are modeled into the proposed control in order to improve control precision when implemented in the field. Local stability and string stability are theoretically proven. The proposed system is evaluated by simulation. Results reveal that the proposed CACC system outperforms the state-of-the-art mathcal {H}_infty synthesis-based controller and linear feedback-based controller. The benefit of fuel consumption reduction ranges from 0.35% to 16.11%, while the benefit of CO2 emission ranges from 0.48% to 12.40%. Furthermore, the proposed CACC improves local stability from 11.03% to 25.90%, and string stability by up to 23.82%. The computation speed of the proposed method is 1.26 ms (with prediction horizon as 1.5 s and resolution as 0.1 s) on a regular laptop which indicates the proposed system's potential to be applied in real-time.

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Communication delay is detrimental to the performance of cooperative adaptive cruise control (CACC) systems. In this paper, we incorporate communication delay explicitly into control design and propose a delay-compensating CACC. In this new CACC system, the semi-constant time gap (Semi-CTG) policy, which is modified on the basis of the widely-used CTG policy, is employed by a linear feedback control law to regulate the spacing error. The semi-CTG policy uses historical information of the predecessor instead of its current information. By doing so, communication delay is fully compensated, which leads to better stability performance. Three stability properties—local stability, string stability, and traffic flow stability—are analyzed. The local stability and string stability of the proposed CACC system are guaranteed with the desired time gap as small as the communication delay. Both theoretical analysis and simulation results show that the delay-compensating CACC has better string stability and traffic flow stability than the widely-used CACC system. Furthermore, the proposed CACC system also shows the potential for improving traffic throughput and fuel efficiency. Robustness of the proposed system against uncertainties of sensor delay and vehicle dynamics is also verified with simulation.

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A cooperative lane-changing (CLC) motion planning algorithm for partially connected and automated environment is proposed in this study. Unlike conventional motion planner whose goal is merely enabling driving maneuver, this proposed algorithm takes one step further in terms of reducing oscillation and shockwave caused by lane change, hence improves transport mobility. The proposed motion planner is designed as a model predictive control which is solved by a dynamic programming-based numerical solution method. Since longitudinal automation is much more accessible than lateral automation, the motion planner requires only longitudinal automation in order to keep the design practical. The proposed motion planner is evaluated against the human driver. Sensitivity analysis is conducted in terms of the initial headway of the receiving gap. The results demonstrate that the motion planner reduces oscillation by 0.1%−9.4%. The variation is due to the changes in initial headway of receiving gap. The computation time is around 17-21 milliseconds showing great potential to be applied in real time.

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In weaving areas, vehicles frequently carry out conflicting lane-changing manoeuvres. The frequent lane change in this area results in rapid changes in vehicles’ speed, which in turn reduces traffic efficiency and create traffic bottlenecks at weaving areas. This research proposes a cooperative weaving motion planner for connected and automated vehicles to reduce traffic oscillation. The proposed motion planner is based on model predictive control method and solved by Chang-Hu’s method. Paper presented at the Intelligent Transportation Systems (ITSC), 2018 IEEE). The motion planner only requires longitudinally automation which is accessible for most commercialized luxury vehicles. Simulation evaluation was conducted to quantify the performance of the proposed motion planner. The results show that the proposed motion planner is able to reduce traffic oscillation by 2.7% to 28.0%. Furthermore, the computation time of the proposed planner is fewer than 20 milliseconds indicating readiness to real-time application.

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This study evaluated the performance of an eco-approach control system at signalized intersections under a partially connected and automated vehicle (CAV) environment. This system has the first eco-approach controller able to function with the existence of surrounding human-driven traffic. A previous evaluation only confirmed its benefits. The purpose of this study was to conduct a further extensive test on the controller to identify room for improvement. Two different networks were tested, including an isolated signalized intersection and a corridor with two signalized intersections. The measures of effectiveness (MOEs) adopted were throughput and fuel consumption. All the before-and-after MOEs were compared using t-tests. The results indicate that the controller generally improved the fuel efficiency without harm to the mobility, and its environmental performance was affected by the minimum CAV speed, green ratio, congestion level, and marker penetration rate of CAVs. A detailed investigation revealed that no significant environmental benefit was generated under high congestion levels when the minimum speed of CAVs was more than 20 mph, and the shockwaves caused by the eco-approach control may result in a gating effect that reduces the throughput at the upstream intersection of the corridor under high congestion levels.

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Cooperative Adaptive Cruise Control (CACC) in previous researches typically refers to the linear controller with a gap policy. The system could not be designed to fulfill multiple objectives. This inspires the concept of optimal control based CACC in this paper. The basic procedure of the proposed controller is to gather the information collected by each vehicle to the computation unit first, then plan the trajectory of all the followers by solving an optimal control problem, and dispatch the optimal motion command to each vehicle at last. This paper models CACC under optimal control framework. A numerical approach inspired by dynamic programming is adopted to solve the control problem. The stability of the proposed controller is thoroughly investigated in terms of both local stability and string stability. To verify the concept of controller, solution, and the analysis about stability, simulation is carried out. The simulation verifies that the numerical method is effective with respect to computation time. Both theoretical analysis and simulation proved that the proposed optimal control based CACC is both local stable and string stable. The low computation burden, local stability, and string stability together guarantee the future implementation of the proposed controller.

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This research proposed an eco-driving system for an isolated signalized intersection under partially Connected and Automated Vehicles (CAV) environment. This system prioritizes mobility before improving fuel efficiency and optimizes the entire traffic flow by optimizing speed profiles of the connected and automated vehicles. The optimal control problem was solved using Pontryagin's Minimum Principle. Simulation-based before and after evaluation of the proposed design was conducted. Fuel consumption benefits range from 2.02% to 58.01%. The CO₂ emissions benefits range from 1.97% to 33.26%. Throughput benefits are up to 10.80%. The variations are caused by the market penetration rate of connected and automated vehicles and v/c ratio. No adverse effect is observed. Detailed investigation reveals that benefits are significant as long as there is CAV and they grow with CAV's market penetration rate (MPR) until they level off at about 40% MPR. This indicates that the proposed eco-driving system can be implemented with a low market penetration rate of connected and automated vehicles and could be implemented in a near future. The investigation also reveals that the proposed eco-driving system is able to smooth out the shock wave caused by signal controls and is robust over the impedance from conventional vehicles and randomness of traffic. The proposed system is fast in computation and has great potential for real-time implementation.

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This research presents an integrated optimal controller to maximize the fuel efficiency of a Hybrid Electric Vehicle (HEV) traveling on rolling terrain. The controller optimizes both the vehicle acceleration and the hybrid powertrain operation. It takes advantage of the emerging Connected Vehicle (CV) technology and utilizes present and future information as optimization input, which includes road topography, and dynamic speed limit. The optimal control problem was solved using Pontryagin's Minimum Principle (PMP). Efforts were made to reduce the computational burden of the optimization process. The evaluation shows that the benefit of the proposed optimal controller is significant compared to regular HEV cruising at the speed limit on rolling terrain. The benefit ranges from 5.0% to 8.9% on mild slopes and from 15.7% to 16.9% on steep slopes. The variation is caused by the change of hilly road density.

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