Modeling bike counts in a bike-sharing system considering the effect of weather conditions

The paper develops a method that quantifies the effect of weather conditions on the prediction of bike station counts in the San Francisco Bay Area Bike Share System. The Random Forest technique was used to rank the predictors that were then used to develop a regression model using a guided forward step-wise regression approach. The Bayesian Information Criterion was used in the development and comparison of the various prediction models. We demonstrated that the proposed approach is promising to quantify the effect of various features on a large BSS and on each station in cases of large networks with big data. The results show that the time-of-the-day, temperature, and humidity level (which has not been studied before) are significant count predictors. It also shows that as weather variables are geographic location dependent and thus should be quantified before using them in modeling. Further, findings show that the number of available bikes at station i at time t-1 and time-of-the-day were the most significant variables in estimating the bike counts at station i.Comment: Published in Case Studies on Transport Policy (Volume 7, Issue 2, June 2019, Pages 261-268

How Do Drivers Behave at Roundabouts in a Mixed Traffic? A Case Study Using Machine Learning

Driving behavior is considered a unique driving habit of each driver and has a significant impact on road safety. Classifying driving behavior and introducing policies based on the results can reduce the severity of crashes on the road. Roundabouts are particularly interesting because of the interconnected interaction between different road users at the area of roundabouts, which different driving behavior is hypothesized. This study investigates driving behavior at roundabouts in a mixed traffic environment using a data-driven unsupervised machine learning to classify driving behavior at three roundabouts in Germany. We used a dataset of vehicle kinematics to a group of different vehicles and vulnerable road users (VRUs) at roundabouts and classified them into three categories (i.e., conservative, normal, and aggressive). Results showed that most of the drivers proceeding through a roundabout can be mostly classified into two driving styles: conservative and normal because traffic speeds in roundabouts are relatively lower than in other signalized and unsignalized intersections. Results also showed that about 77% of drivers who interacted with pedestrians or cyclists were classified as conservative drivers compared to about 42% of conservative drivers that did not interact or about 51% from all drivers. It seems that drivers tend to behave abnormally as they interact with VRUs at roundabouts, which increases the risk of crashes when an intersection is multimodal. Results of this study could be helpful in improving the safety of roads by allowing policymakers to determine the effective and suitable safety countermeasures. Results will also be beneficial for the Advanced Driver Assistance System (ADAS) as the technology is being deployed in a mixed traffic environment

Effect of roundabout design on the behavior of road users: A case study of roundabouts with application of Unsupervised Machine Learning

This research aims to evaluate the performance of the rotors and study the behavior of the human driver in interacting with the rotors. In recent years, rotors have been increasingly used between countries due to their safety, capacity, and environmental advantages, and because they provide safe and fluid flows of vehicles for transit and integration. It turns out that roundabouts can significantly reduce speed at twisting intersections, entry speed and the resulting effect on speed depends on the rating of road users. In our research, (bus, car, truck) drivers were given special attention and their behavior was categorized into (conservative, normal, aggressive). Anticipating and recognizing driver behavior is an important challenge. Therefore, the aim of this research is to study the effect of roundabouts on these classifiers and to develop a method for predicting the behavior of road users at roundabout intersections. Safety is primarily due to two inherent features of the rotor. First, by comparing the data collected and processed in order to classify and evaluate drivers' behavior, and comparing the speeds of the drivers (bus, car and truck), the speed of motorists at crossing the roundabout was more fit than that of buses and trucks. We looked because the car is smaller and all parts of the rotor are visible to it. So drivers coming from all directions have to slow down, giving them more time to react and mitigating the consequences in the event of an accident. Second, with fewer conflicting flows (and points of conflict), drivers only need to look to their left (in right-hand traffic) for other vehicles, making their job of crossing the roundabout easier as there is less need to split attention between different directions

A Comparative Analysis of E-Scooter and E-Bike Usage Patterns: Findings from the City of Austin, TX

E-scooter-sharing and e-bike-sharing systems are accommodating and easing the increased traffic in dense cities and are expanding considerably. However, these new micro-mobility transportation modes raise numerous operational and safety concerns. This study analyzes e-scooter and dockless e-bike sharing system user behavior. We investigate how average trip speed change depending on the day of the week and the time of the day. We used a dataset from the city of Austin, TX from December 2018 to May 2019. Our results generally show that the trip average speed for e-bikes ranges between 3.01 and 3.44 m/s, which is higher than that for e-scooters (2.19 to 2.78 m/s). Results also show a similar usage pattern for the average speed of e-bikes and e-scooters throughout the days of the week and a different usage pattern for the average speed of e-bikes and e-scooters over the hours of the day. We found that users tend to ride e-bikes and e-scooters with a slower average speed for recreational purposes compared to when they are ridden for commuting purposes. This study is a building block in this field, which serves as a first of its kind, and sheds the light of significant new understanding of this emerging class of shared-road users.Comment: Submitted to the International Journal of Sustainable Transportatio

Joint Impact of Rain and Incidents on Traffic Stream Speeds

Unpredictable and heterogeneous weather conditions and road incidents are common factors that impact highway traffic speeds. A better understanding of the interplay of different factors that affect roadway traffic speeds is essential for policymakers to mitigate congestion and improve road safety. This study investigates the effect of precipitation and incidents on the speed of traffic in the eastbound direction of I-64 in Virginia. To the best of our knowledge, this is the first study that studies the relationship between precipitation and incidents as factors that would have a combined effect on traffic stream speeds. Furthermore, using a mixture model of two linear regressions, we were able to model the two different regimes that the traffic speed could be classified into, namely, free-flow and congested. Using INRIX traffic data from 2013 through 2016 along a 25.6-mi section of Interstate 64 in Virginia, results show that the reduction of traffic speed only due to incidents ranges from 41% to 75% if the road is already congested. In this case, precipitation was found to be statistically insignificant. However, regardless of the incident impact, the effect of light rain in free-flow conditions ranges from insignificant to a 4% speed reduction while the effect of heavy rain ranges from a 0.6% to a 6.5% speed reduction when the incident severity is low but has a roughly double effect when the incident severity is high.</p

Temporal Shifts in E-Scooter Rider Perspectives: A Longitudinal Investigation in Riyadh, Saudi Arabia

Shared electric scooters (e-scooters) have rapidly gained prominence as a first/last-mile mobility solution globally, with over 66,000 systems operating in 88 cities across 21 countries in 2019. While recognized for their flexibility, accessibility, and environmental benefits, concerns such as safety, parking issues, and infrastructural challenges accompany the operation of shared e-scooter systems. This research investigates the evolving perceptions of e-scooter users in Riyadh, Saudi Arabia, comparing pre-survey results with a recent study following the official deployment of e-scooters as a transportation mode in 2022. The analysis reveals significant shifts in user behavior, preferences, and perceptions. The findings indicate increased familiarity with e-scooters, heightened usage rates, and notable changes in domestic e-scooter use. Furthermore, the study identifies variations in willingness to use e-scooters across genders. A notable shift is observed in riders’ perceptions, transforming from viewing e-scooters primarily as entertainment tools to embracing them as a reliable mode of transportation. The results show that the percentage of female respondents using e-scooters increased from 3% to 13%, representing over four times the post-survey numbers. Additionally, the percentage of individuals perceiving e-scooters as safe decreased from 28.2% in the pre-survey to 14.9% in the current survey (post-survey) among those who had used e-scooters. The regression analysis demonstrates a historical uptrend in the utilization of e-scooters, juxtaposed with a discernible decline projected for forthcoming usage (odds ratio [OR] = 0.74). Intriguingly, there is evidence indicating an enhancement of riders’ confidence towards e-scooters, as reflected by an augmented perception of safety (OR = 1.48)