1. 一位货车司机的生死27小时: 不超载，没钱赚

  2. 为何一到周末，广州地铁客流就反超京沪？

  3. 谷歌母公司计划开发多伦多智慧城市

    人行道实验室发布了 483 页的文件，以回应政府授权机构在该项目初始时对其开发提出的「过于抽象」的批评。

  4. Editorial: “Psychophysiological Contributions to Traffic Safety”

    • how mental states can be optimally tracked in simulated as well as naturalistic contexts; now and when technology progresses further towards autonomous driving.

  5. 福特刚发布一款马车，和特斯拉一样能跑

  6. Fully autonomous cars could be on open roads within 5 years, says self-driving start-up Pony.ai CEO

  7. Kalter, Marie-José Olde, Lissy La Paix Puello, and Karst T. Geurs. “Do changes in travellers’ attitudes towards car use and ownership over time affect travel mode choice? A latent transition approach in the Netherlands.” Transportation Research Part A: Policy and Practice 132 (2020): 1-17.

    • Research question: This paper examines how changes in travellers’ attitudes towards car use and ownership change over time and how these changes influence car use
    • Findings:
      • Four latent classes were found to reflect the participants’ attitudes: cost-sensitive, car-minded, environmentally aware and social-conscious travellers
      • Only when younger adults face life events, such as moving, starting a job or become parents, transitioning to more car-oriented profiles appears more likely.
      • Changes in attitudes towards car use and car ownership do not significantly affect car use (number of trips per day), except for the social-conscious travellers who switched to the car- minded class

  8. Watch Tesla retrofit Model X with new Full Self-Driving Computer

  9. 中国制造Model 3即将全面到店，周五见！

  10. How Selfish Are You? It Matters for MIT’s New Self-Driving Algorithm

  11. Liu, Yang, et al. “Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge Solution.” arXiv preprint arXiv:1911.05699 (2019).

    • Motivation: How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem
    • Research goal: This study focuses on the construction of an effective solution designed for spatio-temporal data to predict large- scale traffic state.
    • Methods: We adopt a structure similar to U-net and use a mask instead of spatial attention to address the data sparsity. Then, combined with the experience of time series prediction problem, we design a number of features, which are input into the model as different channels. Region cropping is used to decrease the difference between the size of the receptive field and the study area, and the models can be specially optimized for each sub-region. The fusion of interdisciplinary knowledge and experience is an emerging demand in classical traffic research

  12. Pereira, Francisco C., Ana LC Bazzan, and Moshe Ben-Akiva. “The role of context in transport prediction.” IEEE Intelligent Systems 29.1 (2014): 76-80.

    • transport prediction combine context information from internet.
      • information retrieval
      • information extraction
      • transportation prediction with context

  13. Rodrigues, Filipe, et al. “A Bayesian additive model for understanding public transport usage in special events.” IEEE transactions on pattern analysis and machine intelligence 39.11 (2016): 2113-2126.

    • Motivation: Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise
    • Problem: Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinate. The problem highly increases when several events happen concurrently.
    • Methods:
      • This paper presents a Bayesian additive model with Gaussian process components that combines smart card records from public transport with context information about events that is continuously mined from the Web.
      • We develop an efficient approximate inference algorithm using expectation propagation, which allows us to predict the total number of public transportation trips to the special event areas, thereby contributing to a more adaptive transportation system.
      • Furthermore, for multiple concurrent event scenarios, the proposed algorithm is able to disaggregate gross trip counts into their most likely components related to specific events and routine behavior.

  14. Markou, Ioulia. Detection, analysis and prediction of traffic anomalies due to special events. Diss. Technical University of Denmark, 2019.

    • Demand pattern analysis of taxi trip data for anomalies detection and explanation
    • Predicting taxi demand hotspots using automated Internet Search Queries

  15. Uber自动驾驶车祸判决：人没监督车？车没监督人？

  16. 蔚来李斌：个人购车需求强烈，电动汽车的春天已经不远

  17. 日烧100万美元，这家第四大网约车平台倒闭了

  18. Didi will soon roll out a self-driving taxi service in Shanghai

  19. Ye, Runing, Jonas De Vos, and Liang Ma. “Analysing the association of dissonance between actual and ideal commute time and commute satisfaction.” Transportation Research Part A: Policy and Practice 132 (2020): 47-60.

    • Dissonance between actual and ideal commute time negatively affects commute satisfaction.
    • Active travellers have higher level of consistency between actual and ideal commute duration than motorised travellers.
    • Commute time dissonance partly mediated the effect of actual commute time on travel satisfaction.

  20. Molin, Eric, et al. “Does conducting activities while traveling reduce the value of time? Evidence from a within-subjects choice experiment.” Transportation Research Part A: Policy and Practice 132 (2020): 18-29.

    • Evidence found for hypothesis that conducting onboard activities decreases VoT.
    • Conducting activities while traveling reduces VoT of commuters by 30%.
    • VoT reductions can be regarded as the monetary Value of Activities.
    • Within-subjects experiments are essential to study impact activity context on VoT.

  21. 普快列车票为什么20年来从未涨过价？

  22. 赛博朋克皮卡，马斯克式疯狂

  23. Edge Cases and Autonomous Vehicle Safety