Dr Haoning Xi

The COVID-19 pandemic has resulted in substantial negative impacts on social equity. To investigate transport inequities in communities with varying medical resources and COVID controlling measures during the COVID pandemic and to develop transport-related policies for the post-COVID-19 world, it is necessary to evaluate how the pandemic has affected travel behavior patterns in different socio-economic segments (SES). We first analyze the travel behavior change percentage due to COVID, e.g., increased working from home (WFH), decreased in-person shopping trips, decreased public transit trips, and canceled overnight trips of individuals with varying age, gender, education levels, and household income, based on the most recent US Household Pulse Survey census data during Aug 2020 ~ Dec 2021. We then quantify the impact of COVID-19 on travel behavior of different socio-economic segments, using integrated mobile device location data in the USA over the period 1 Jan 2020¿20 Apr 2021. Fixed-effect panel regression models are proposed to statistically estimate the impact of COVID monitoring measures and medical resources on travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH for low SES and high SES. We find that as exposure to COVID increases, the number of trips, traveling miles, and overnight trips started to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and did not tend to return to pre-COVID level. We find that the increase in new COVID cases has a significant impact on the number of work trips in the low SES but has little impact on the number of work trips in the high SES. We find that the fewer medical resources there are, the fewer mobility behavior changes that individuals in the low SES will undertake. The findings have implications for understanding the heterogeneous mobility response of individuals in different SES to various COVID waves and thus provide insights into the equitable transport governance and resiliency of the transport system in the ¿post-COVID¿ era.

Researches showed that it was difficult to achieve objectives of reducing congestion and emission simultaneously. Road congestion pricing can manage the traffic demand efficiently, and thus reduce congestion, but it may not decrease vehicular emissions. The objectives of this study are multi-class users: users with different value of time (multi-VOT users) and users with different vehicle types (multi-vehicle users). By establishing the biobjective optimization model considering congestion and emissions simultaneously, it is proved that the Paretoefficient link flow can be decentralized as multi-class user equilibrium flow pattern by an effective road pricing scheme, whatever time cost criterion or monetary cost is used to choose the route, and thus the congestion and emissions can be reduced simultaneously.

Increasing traffic congestion and vehicular emissions have become a major public concern, which naturally leads to a bi-objective optimization problem, i.e., to minimize system total travel time and system total emissions. In this study, a road pricing scheme is proposed to manage the system's total travel time and total emissions simultaneously. Finally, the practical case study based on Chengdu in China is carried out to simulate a situation with the proposed road pricing. Simulation results evaluate the effectiveness of the road pricing scheme to manage the congestion and emissions.

The keep-right rule is widely implemented in traditional traffic systems, but it might not be the optimal one in intelligent vehicle-infrastructure cooperation systems (i-VICS). According to literature reviews, the free rule is more effective in i-VICS, due to its superior mutual information communication. This paper firstly analyzes the reason why keep-right rule is more popular in traditional traffic systems, and then compares the overtaking procedure under the keep-right rule in traditional traffic systems and free rule in i-VICS. Comparison results show that overtaking in i-VICS is more efficient. Furthermore, five improved alternatives considering speed constraints and vehicle types are proposed and simulated through VISSIM. After this, the simulation results are used to evaluate alternative rules in i-VICS through the TOPSIS method. Additional sensitivity analysis shows that rule 5 is more effective in i-VICS.

Governments all over the world have attached great importance to the management of long-distance transport drivers. According to the statistics, the most dangerous three behaviors of long-distance drivers are: fatigued driving, distracted driving, and unrestricted driving. In this paper, these three behaviors are defined as safety features of drivers, which are detected based on machine learning and image processing technology. For identity recognition of drivers, Eigenface, Fisherface, and LBPH algorithms are combined to achieve a recognition rate of 100%. Driving time of each driver is recorded automatically. For mobile phone detection of drivers, the HOG algorithm and SVM algorithm are combined to achieve the recognition rate of 80%. For unrestricted driving detection, Gray-level integral projection method is improved to raise the detection rate to 85%. Finally, the safety feature detection system for drivers is developed to ensure the safety of long-distance transport.