Youssef Bichiou

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

The current design practice for the vertical profile of roads in rolling and mountainous terrains is to follow the existing grades in order to minimize earthwork costs. This means that the only criterion considered during the design phase is the initial cost. It would be preferable to include other criteria that are directly related to sustainability, particularly fuel consumption and CO2 emissions. Therefore, this paper describes a proposed design procedure that starts by finding feasible alternatives with different grades. Then, a microsimulation traffic tool is used to simulate the movement of predicted vehicles (volume and type) over the different alternatives. The microsimulation tool provides reliable estimates of travel times, fuel consumption, and CO2 emissions for the different alternatives. With these data, it is possible to use life cycle cost analysis (LCCA) or multiple criteria decision aid (MCDA) tools to select the “optimal” alternative. The proposed procedure was used on a case study involving a 6-km highway section with different proposed grades ranging from 2% to 8%. Using LCCA and an MCDA tool, it was revealed that the current design alternative is not the optimal alternative in most considered scenarios (various fuel values for LCCA and different “Cost” weights for MCDA).

The time gap is defined as the time difference between the rear of a vehicle and the front of its follower, which affects both safety and the saturation flow rate of a roadway segment. In this study, naturalistic driving data were examined to measure time gaps from seven different drivers in a car-following scenario within steady-state conditions. The measurements were taken from a 13-km section of a Dulles Airport access road in Washington, DC. In total, 168,053 time gap samples were obtained covering seven speed intervals. Analysis of the data revealed a large variation in time gaps within individual drivers’ driving data, with coefficients of variation as high as 63.8% observed for some drivers. Results also showed that the variability within drivers was more significant at speeds higher than 54 km/h. In addition, there was a large variability between drivers. At speeds above 108 km/h, minimum time gaps left by some drivers could be 1.6 times longer than those left by others. Several statistical distributions were used to fit the data of the seven drivers as well as the data for all drivers combined for each speed interval. The selected distributions passed the goodness-of-fit (Kolmogorov-Smirnov, Chi-square, and Anderson-Darling) criteria only when the number of samples was reduced. Data reduction was not performed randomly, but rather in a manner intended to maintain the same observed distribution when all the samples were used. It is therefore recommended that empirical measures of distributions be used in traffic microsimulation software rather than theoretically fit distributions obtained based on statistical tests. This will lead to better naturalistic traffic behavior simulations, resulting in more precise predicted measures of performance (travel time, fuel consumption, and gas emissions).

Connected vehicle technologies enable and encourage cooperative driving behaviors, as well as provide potential benefits to improve mobility and safety. Using cutting-edge information and communication technology (ICT), various connected vehicle (CV) applications have been developed and are being prepared for deployment in transportation systems. This study proposes an in-vehicle optimal lane selection (OLS) application as one realizable and practical CV application. The OLS application advises the driver of the optimal lane to travel efficiently and minimize the number of lane changes. In order to choose the optimal lane, a utility function uses real-time data collected through connected technologies. This study implements and evaluates the proposed application using a microscopic traffic simulator, INTEGRATION. The simulation was conducted on a freeway network using various demand levels and market penetration rates (MPRs). Results show that the OLS application in the CV environment had better performance in lane allocation and a decrease of travel time compared to the lane-changing logic of the traditional transportation system.

In recent years, automated vehicles (AVs) are emerging as a realistic and viable option. Consequently, substantial research efforts are being dedicated to the development of systems and technologies for the field implementation of AVs. One of the challenges researchers need to address is how to optimize the movement of AVs through roadway intersections. In this paper, an attempt to address this need is introduced. The developed model is an optimization problem subjected to dynamical constraints (i.e., ordinary differential equations governing the motion of a vehicle) and static constraints (i.e., maximum achievable velocities). By virtue of Pontryagin's minimum principle, the solution that minimizes the trip time is obtained. Given the problem parameters, the solution to the formulated problem is expected to be the true optimum and delivers the lowest possible delay that satisfies the previously mentioned constraints. This logic is simulated and compared with the operation of a roundabout, a stop sign, and a traffic signal-controlled intersection. The results demonstrate that an 80% reduction in delay is achievable compared with the best of these three intersection control strategies, on average. An interesting byproduct of this new logic is a reduction in vehicular fuel consumption and CO₂ emissions by 42.5% and 40%, respectively.