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樓主: 叛亂分子
51#
發(fā)表于 2025-3-30 11:24:04 | 只看該作者
Modelling and Simulation of Speed Guidance of Multi-Intersection in a Connected Vehicle Environmentvehicles’ delay or stopping time in the environment of connected vehicles. Using the vehicle’s position, speed, and real-time traffic signal information (signal light’s color and its’ remaining time) obtained by communication of infrastructure to connected vehicle, speed advisory system can produce
52#
發(fā)表于 2025-3-30 13:51:38 | 只看該作者
Pattern Mining and Predictive Inference on Short-Term Weather and Collision Time Series Data,onal highway network, are highly random and fiercely fluctuated. The descriptive and inferential analyses for this type of short-term collision time series data, abbreviated as SCTS data in this paper, are still not well-established yet. This paper is to tackle this issue by a newly emerging approac
53#
發(fā)表于 2025-3-30 16:31:24 | 只看該作者
54#
發(fā)表于 2025-3-30 22:29:56 | 只看該作者
55#
發(fā)表于 2025-3-31 03:01:53 | 只看該作者
56#
發(fā)表于 2025-3-31 06:57:05 | 只看該作者
An Agent-Based Cellular Automata Model for Urban Road Traffic Flow Considering Connected and Automaded period, urban roads will be in a mixed traffic flow scene where CAVs and human-driven vehicles (HDVs) coexist. This paper uses an agent-based cellular automata model to establish a micro-traffic simulation framework for urban roads, called the ABCA-MS model. Considering the characteristics of th
57#
發(fā)表于 2025-3-31 12:41:15 | 只看該作者
58#
發(fā)表于 2025-3-31 14:27:37 | 只看該作者
Research on Investment Benefits Valuation Methods for Information Construction of Integrated Passento improve the integration level of regional transportation and the quality of passenger travel service. However, Our country is currently in a period of economic structural adjustment, financial constraints, limited financial support for the informatization construction of integrated passenger tran
59#
發(fā)表于 2025-3-31 20:03:55 | 只看該作者
Learning Individual Travel Pattern by Using Large-Scale Mobile Location Data with Deep Learning,tween mobile location data and individual travel patterns. This paper proposes a novel deep learning framework to extract individual travel patterns by using large-scale mobile location data. The proposed framework includes methods for extracting origin and destination points based on spatiotemporal
60#
發(fā)表于 2025-3-31 23:30:58 | 只看該作者
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