New publication in Transport Policy (Part II)

The following paper is now available online!

Chikaraishi, M., Garg, P., Varghese, V., Yoshizoe, K., Urata, J., Shiomi, Y., Watanabe, R. (2020) On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis, Transport Policy (Accepted).

Abstract: Since the cost and time required to finetune parameters in traditional short-term traffic prediction models such as traffic simulators are very high, the prediction models have been developed mainly for managing recurrent congestion, rather than non-recurrent congestion caused, for example, by disaster. Machine learning models are promising candidates for traffic prediction during non-recurrent congestion due to their ability to tune parameters without a-priori knowledge, while their applicability to non-recurrent conditions has rarely been explored. To fill in this gap, this study conducts an exploratory analysis on the applicability of various machine learning models during a transportation network disruption with particular focuses on their ability to predict traffic states and the interpretability of the results. The analysis is conducted by using data obtained during the massive transport network disruption which occurred in Hiroshima in July 2018 due to heavy rain and subsequent landslides. The models tested include random forest, support vector machine, XGBoost, shallow feed-forward neural network, and deep feed-forward neural network. The results indicate that random forest and XGBoost methods produced the best results in terms of prediction accuracy. On the other hand, deep neural network models produce better results in terms of the interpretability of the results, i.e., the results can be logically explained from the perspective of existing traffic flow theory. These findings indicate that the model which produces the best prediction accuracy is not always the best for practical use since it does not mimic the mechanisms of congestion occurrence.

https://www.sciencedirect.com/science/article/abs/pii/S0967070X20304194

New publication in transport Policy

The following paper has been published.

Chikaraishi, M., Khan, D., Yasuda, B., Fujiwara, A. (2020) Risk Perception and Social Acceptability of Autonomous Vehicles: A Case Study in Hiroshima, Japan, Transport Policy (In Press).

Abstract
Given the impending introduction of self-driving cars to Japan within the next several years, gaining a better understanding of public opinion and risk perception of autonomous vehicles (AVs) is crucial. Though AVs have numerous potential social and economic benefits, including reduced travel time and environmental impacts, their implementation will depend on public acceptance. This study expanded on existing work by directly examining which aspects of AV use and function most affect risk perception. Participants were shown short animated video clips depicting the introduction of AVs into society at large, as well as three specific possible risk factors: system error, external interference with car controls (i.e., hacking), and the inability of the car to cope with unexpected events. Participants were then surveyed about their attitudes toward AVs and other potentially risky activities and technologies. The study established that the perceived advantages of all AV types (cars and buses, different automation levels) outweighed their perceived risks. Consistent with prior research, the two major aspects of perceived risk were dread and unfamiliarity. The results showed compared with other technologies, AV scores were neutral for dread risk but higher for unfamiliarity risk. The finding of high unfamiliarity indicates that public acceptance and perceived risks are likely to change as the public’s knowledge increases. We also found that receiving information about a potential system error indirectly reduced AV acceptability, where dread and unfamiliarity to the AV risks served as mediators. The results suggest that proper management on the diffusion of information, which includes public information campaigns, test-ride events and transparency about safety options, will likely influence the ultimate social acceptability of AVs and will be crucial towards its successful introduction on the market.

https://www.sciencedirect.com/science/article/abs/pii/S0967070X20303589