Existing lane-changing (LC) algorithms in general prioritize self-interest above the benefits of others. We argue that an autonomous vehicle should be socially responsible during lane change without causing excessive impact on its surroundings. Thus, in this paper, we propose an LC algorithm that could generate a trajectory to optimize the overall benefits of the subject vehicle and others in its vicinity. This is mainly achieved by introducing Longitudinal Control Model to characterize the driving behaviors of neighboring vehicles, and drivers’ needs such as comfort, efficiency, and safety are simultaneously satisfied. We combine macro and micro comparative analysis to evaluate the advantage of our approach against existing algorithms. Our findings reveal that the vehicle controlled by our algorithm is capable of safely performing the LC maneuver while causing the least amount of impact. This is reflected in the decrease in total cost of all vehicles and the increase in traffic speed and throughput in its vicinity. In addition, we use HighD dataset for further validation and demonstrate that our algorithm is practically sound. Application of this research can lead to improved autonomous driving technology.

As one of the key algorithms in supporting AV (autonomous vehicle) to complete the LC (lane changing) maneuver, the LTP (LC trajectory planning) algorithm generates safe and efficient LC trajectory for the AV. This paper proposes a novel dynamic LTP algorithm based on the quintic polynomial curve. This algorithm is capable of adjusting LC trajectory according to the state changes of the surrounding driving environment. The formulation of our proposed algorithm mainly consists the underlying form of trajectory equation, the optimization objective function, the corresponding constrains, and the SQP (sequential quadratic programming) algorithm. For each planning step, the time-based quintic polynomial function is introduced to model the trajectory equation. The problem of solving the parameters of the corresponding equation is then transformed into an optimization problem, which takes driver’s safety, comfort, and efficiency into account. After that, the SQP algorithm is employed to solve this optimization problem. Finally, both numerical simulation and field-data validation are used to verify the effectiveness of our proposed algorithm. We anticipate that the research could provide certain valuable insights for developing more reliable LC algorithms for AVs.

Understanding mechanism of daily activity trip chain especially multi-activity trip chain is significant for travel demand management and urban redevelopment. This paper explores the multi-activity trip chain behavior from both individual attributes and spatial attributes. Multiactivity trip chain is identified from people’s full-day travel diaries, then categorized into multi-activity intermittent trip chain and continuous trip chain to distinguish the characteristics and influencing factors of different chains. Using resident travel survey data of Xiaoshan District of Hangzhou, China, multinomial logistic regression model, transition probability matrix and activity analysis methods are employed to make analyses. Findings include: 1) making multi-activity trip chain can achieve multiple purposes with less average travel time for each purpose, but public transit is less used. 2) The choices of single or multi-activity trip chain and multi-activity intermittent or continuous trip chain are mainly affected by different individual attributes such as occupation, education, household income, etc. Moreover, household registration, driver’s license, gender, and household car ownership are related to the choice of activity sequence. 3) For typical trip chain with purposes of work, shopping/dining, and home, activity sequence is also obviously influenced by spatial distance between origin and destination, especially for the chain of work-shopping/dining-home.