The paper proposes a car following model from the perspective of visual imaging (VIM), where the visual imaging size of the preceding vehicle on a driver's retina is considered as the stimuli and determines the driving behaviors. NGSIM trajectory data are applied to calibrate and validate the VIM under two scenarios, i.e. following the car and following the truck, whose fitting performance outperforms that of visual angle car following model (VAM). Through linear stability analyses for VIM, it can be drawn that the asymmetry in traffic flow is preserved; the larger vehicle width, vehicle length and vehicle apparent size all benefit enlarging the traffic flow stable region; the traffic flow unstable region when following the car tends to fall in the relatively small distance headway range compared with that when following the truck. After that, numerical experiments demonstrate that the visual imaging information applied in VIM is more contributive to the traffic flow stability than the visual angle information in VAM when following the truck in the relatively large distance headway or involving the driver's perception threshold, i.e. Weber ratio; introducing Weber ratio would break the originally stable traffic flow or deteriorate the traffic fluctuation, which however can be alleviated by increasing drivers' sensitivity, e.g., decreasing Weber ratio. Finally, VIM is verified to be able to satisfy the consistency criteria well from the theoretical aspect.
Taxi is an indispensable mode in the urban public transportation. Although many studies have explored the travel patterns of taxi trips, few have combined taxi and subway to reveal their intermodal relationship. To bridge the gap, this study utilized taxi’s trajectory data to investigate its relationship with subway. Considering the multifaceted relationship between taxi and subway in operation, taxi trips are categorized into three types, namely, subway-competing, subway-extending, and subway-complementing taxi trips. The characteristics of each type of taxi trips reflect the specialties and their interactions with subway. The origin/destination distributions of taxi and subway trips are compared and analyzed. Furthermore, the supply and demand of taxi within the buffer zone of each subway station are analyzed to reflect the difficulty of hailing taxis. The negative binomial regression models are used to explore the relationship between taxi trips and subway ridership. The results show that there is a significantly positive correlation between taxi trips and subway ridership.
The transportation sector significantly contributes to greenhouse gas emissions, necessitating accurate emission models to guide mitigation strategies. Despite its field validation and certification, the industry-standard Motor Vehicle Emission Simulator (MOVES) faces challenges related to complexity in usage, high computational demands, and its unsuitability for microscopic real-time applications. To address these limitations, we present NeuralMOVES, a comprehensive suite of high-performance, lightweight surrogate models for vehicle CO2 emissions. Developed based on reverse engineering and Neural Networks, NeuralMOVES achieves a remarkable 6.013% Mean Average Percentage Error relative to MOVES across extensive tests spanning over two million scenarios with diverse trajectories and the factors regarding environments and vehicles. NeuralMOVES is only 2.4 MB, largely condensing the original MOVES and the reverse engineered MOVES into a compact representation, while maintaining high accuracy. Therefore, NeuralMOVES significantly enhances accessibility while maintaining the accuracy of MOVES, simplifying CO2 evaluation for transportation analyses and enabling real-time, microscopic applications across diverse scenarios without reliance on complex software or extensive computational resources. Moreover, this paper provides, for the first time, a framework for reverse engineering industrial-grade software tailored specifically to transportation scenarios, going beyond MOVES. The surrogate models are available at https://github.com/edgar-rs/neuralMOVES.
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