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Understanding human mobility is of great significance for sustainable transportation planning. Long-term travel delay change is a key metric to measure human mobility evolution in cities. However, it is challenging to quantify the long-term travel delay because it happens in different modalities, e.g., subway, taxi, bus, and personal cars, with implicated coupling. More importantly, the data for long-term multi-modal delay modeling is challenging to obtain in practice. As a result, the existing travel delay measurements mainly focus on either single-modal system or short-term mobility patterns, which cannot reveal the long-term travel dynamics and the impact among multi-modal systems. In this paper, we perform a travel delay measurement study to quantify and understand long-term multi-modal travel delay. Our measurement study utilizes a 5-year dataset of 8 million residents from 2013 to 2017 including a subway system with 3 million daily passengers, a 15 thousand taxi system, a 10 thousand personal car system, and a 13 thousand bus system in the Chinese city Shenzhen. We share new observations as follows: (1) the aboveground system has a higher delay increase overall than that of the underground system but the increase of it is slow down; (2) the underground system infrastructure upgrades decreases the aboveground system travel delay increase in contrast to the increase the underground system travel delay caused by the aboveground system infrastructure upgrades; (3) the travel delays of the underground system decreases in the higher population region and during the peak hours.

 

With improved portability and affordability, eye tracking devices have facilitated an expanding range of cycling experiments aimed at understanding cycling behavior and potential risks. Given the complexity of cyclists’ visual behavior and gaze measurements, we provide a comprehensive review with three key focuses: 1) the adoption and interpretation of various gaze metrics derived from cycling experiments, 2) a summary of the findings of those experiments, and 3) identifying areas for future research. A systematic review of three databases yielded thirty-five articles that met our inclusion criteria. Our review results show that cycling experiments with eye tracking allow analysis of the viewpoint of the cyclist and reactions to the built environment, road conditions, navigation behavior, and mental workload and/or stress levels. Our review suggests substantial variation in research objectives and the consequent selection of eye-tracking devices, experimental design, and which gaze metrics are used and interpreted. A variety of general gaze metrics and gaze measurements related to Areas of Interest (AOI) are applied to infer cyclists’ mental workload/stress levels and attention allocation respectively. The diversity of gaze metrics reported in the literature makes cross-study comparisons difficult. Areas for future research, especially potential integration with computer vision are also discussed. 

We analyze the effect of a bicycle lane on traffic speeds. Computer vision techniques are used to detect and classify the speed and trajectory of over 9,000 motor-vehicles at an intersection that was part of a pilot demonstration in which a bicycle lane was temporarily implemented. After controlling for direction, hourly traffic flow, and the behavior of the vehicle (i.e., free-flowing or stopped at a red light), we found that the effect of the delineator-protected bicycle lane (marked with traffic cones and plastic delineators) was associated with a 28 % reduction in average maximum speeds and a 21 % decrease in average speeds for vehicles turning right. For those going straight, a smaller reduction of up to 8 % was observed. Traffic moving perpendicular to the bicycle lane experienced no decrease in speeds. Painted-only bike lanes were also associated with a small speed reduction of 11–15 %, but solely for vehicles turning right. These findings suggest an important secondary benefit of bicycle lanes: by having a traffic calming effect, delineated bicycle lanes may decrease the risk and severity of crashes for pedestrians and other road users. 

Micromobility usage has increased significantly in the last several years as exemplified by shared escooters and privately owned bicycles. In this study, we use traffic camera footage to observe the behavior of over 700 shared e-scooters and privately owned bicycles in Asbury Park, New Jersey. We address the following questions: (1) What are the behavioral differences between bicycle and e-scooter usage in terms of helmet use, bike lane / sidewalk use, gender split, group riding, and by time of day? (2) Are more protective conditions associated with helmet use and bike lane / sidewalk use? And (3) what is the gender split between e-scooter users and cyclists? We find notable differences in safety precautions: around one third of cyclists but no shared e-scooter users were observed wearing a helmet. Among cyclists, helmet use was more prominent among men than women. However, men were more likely to ride on the road than women. We also found that the gender split was narrower among e-scooter users, with a nearly even gender split – as opposed to cyclists, where only 21% of cyclists were observed to be women. Our findings suggest that e-scooter users take fewer safety precautions, in that they are less likely to use a bike lane and to wear a helmet. We conclude with policy implications with regards to safety and gender differences between these two modes.