Meng Li

Emission Mitigation via Longitudinal Control of Intelligent Vehicles in a Congested Platoon

Drivers’ car-following behavior, coupled with inaccurate perception of ambient conditions, contributes to oscillations and significant emissions in congested highway traffic. The emergence of intelligent vehicles and communication technologies provide an opportunity to dynamically optimize longitudinal control of car-following vehicles in a platoon and eliminate human factors from car-following laws. This paper proposes a nonlinear model predictive control (MPC) model for emission mitigation via longitudinal control of intelligent vehicles in a congested platoon. To relieve the real-time optimization burden, the authors also propose an instantaneous control model which is essentially a simplified MPC model with short prediction and control horizons. The proposed vehicle control strategies are tested in a series of simulation scenarios, and the results verify that the localized and instantaneous control of a few intelligent vehicles could induce lower emissions of a platoon of vehicles. The proposed models are also applied to field trajectory data, and results show that the instantaneous emission optimization model significantly reduces emissions without increasing travel time. In addition, numerical results show that the effectiveness of the proposed vehicle longitudinal control on emission mitigation and traffic stabilization increase with the percentage (penetration rate) of intelligent vehicles in the congested vehicle platoon.

Social Welfare Maximization of Multimodal Transportation: Theory, Metamodel, and Application to Tianjin Ecocity, China

Multimodal urban transportation systems exhibit complex inter actions between components, including users, multimodal transportation facilities, supply side agencies, and operators. Although these interactions are obvious, rigorous quantitative methods for optimizing control variables across modes of transportation on real-world networks are deficient. A social welfare maximization model was established for joint optimization of bus fare, rail transit fare, and congestion tolls for private cars. The authors determined the optimality conditions and the second-order partial derivatives for this optimization problem. Because of the complexity of the multimodal urban transportation system, the objective function of social welfare has no closed form and is extremely expensive to evaluate. The authors therefore proposed a simulation-based framework for evaluation of the objective function and optimization of three decision variables across multiple travel modes. Several metamodels (i.e., mathematical functions that approximate the true shape of an unknown, nonlinear, and complex objective function) were adopted to approximate the highly nonlinear input–output mappings in the urban system. This is the first study to develop a simulation-based method for joint optimization of transit and road network operations. The case study applied the simulation-based optimization framework to the Sino–Singapore ecocity in Tianjin, China, by using VISSUM as the urban systems simulator. An “ecocity” is defined by the authors as “a thriving city that is socially harmonious, environmentally friendly, resource-efficient, and a model for sustainable development.” Results show that metamodels can accurately approximate the real objective function and produce good suboptimal and near-optimal solutions. The optimal combination of transit fares and congestion tolls significantly outperform those under two baseline scenarios. The optimal solutions also suggest that extreme transit fares (too high or too low) or congestion tolls are contrary to welfare-maximizing objectives. The proposed method can be applied for joint optimization of other multimodal planning and operational strategies, such as investment and operational decisions across various modes of transportation.

Evaluation of Accident-Induced Indirect Costs for Measuring Penalties on Violations of Laws

Traffic accidents cause significant economic losses, which include not only direct costs, such as damage to vehicles and road facilities, but also indirect costs, such as traffic delays for other travelers. For severe accidents involving violations of laws, the direct economic loss is trivial compared with the indirect economic loss resulting from long hours of congestion and large numbers of affected travelers. Drivers who violate transportation laws should be subject to legal liabilities for both direct and indirect economic losses. To calculate the penalties for violating laws related to traffic accidents, it is important to establish a standard for evaluating the indirect costs of accidents. A general framework for estimating accident-induced indirect costs is proposed. As a key component of the estimation procedures, the delay estimation can be conducted by reconstructing the spatial–temporal traffic flow evolutions on the basis of intelligent transportation system sensors or archived data, or both. The proposed evaluation methodology, which measures on-site arrival–departure cumulative flows and speeds, was compared with the spatial–temporal traffic state evolution approach. As a case study, the proposed approach was applied to two severe traffic accidents that occurred on the G6 Highway (Beijing–Tibet Highway). The promising results verify the feasibility and accuracy of the proposed procedures. The analytic study results also support drafting the related national standard in China.

Operational redistribution model for a large-scale bicycle-sharing system: Case Study in China

In recent years, bicycle-sharing systems (BSSs) have been getting more and more popular in many cities all over the world, particularly in developing countries. However, a significant operating problem was the imbalance that occurred in the distribution of bicycles, especially in large-scale BSSs during peak hours. This problem could significantly reduce the level of service and number of potential users. To improve the level of service of BSSs, the necessity of redistribution was analyzed, and an operational redistribution model (ORM) that could deal with large-scale redistribution was developed. The objective of the ORM was to minimize the generalized operation costs of BSSs, which were the penalty cost of unserved user demand and the cost of redistribution of bicycles. The overall system performance was analyzed under several scenarios. The results demonstrated that an ORM could effectively improve the level of service of a BSS and could provide a detailed work plan for each redistribution truck to implement. For redistribution in a large-scale BSS, the partition of subzones was important to achieve a high level of service with relatively low generalized costs. In addition, an optimal number of subzones could be found through the scenario-based optimization process.

Comparison of Highway Traffic Breakdown Features between U.S. and China Using Discrete Wavelet Transform: An Empirical Study

This paper provides empirical observations and measurements on highway traffic breakdown using discrete wavelet transform for revealing some important features related to breakdown activation and recovery process during transitions from free-flow traffic to congested states. The method effectively distinguishes useful breakdown information from nonlinear and nonstationary speed time series. Comparatively, the analysis of loop detector data from two highways (I80-W in Berkeley, CA, U.S. and G6-S in Beijing, China) shows that the wavelet-based energy approach enables effective identification of traffic breakdown occurrence time, recovery time, state transition time, and congested speed. Observations show that I80-W performs longer transition time and recovery time in traffic breakdown than G6-S, from which it is inferred that traffic breakdown occurs and recovers more abruptly on G6-S. The empirical study has important implications for understanding the breakdown process and statistical characteristics of traffic state transitions.