Measuring the impact of ride-hailing firms on urban congestion: The case of Uber in Europe

This paper examines the impact of Uber, the world's largest ride-hailing firm, on congestion. Drawing on data from European cities for the period 2008 through 2016, I find a negative impact of Uber on congestion. The estimated impact in the baseline regression is 3.5 percentage points, but it is higher in cities that do not impose strong regulatory restrictions to ride-hailing services. In addition, the negative impact of Uber on congestion is only statistically significant in denser cities. The Uber effect is gradual given that its impact increases over time. Finally, I find suggestive evidence that the potential endogeneity bias underestimates the negative effect of Uber on congestion

Real-time traffic event detection using Twitter data

Incident detection is an important component of intelligent transport systems and plays a key role in urban traffic management and provision of traveller information services. Due to its importance, a wide number of researchers have developed different algorithms for real-time incident detection. However, the main limitation of existing techniques is that they do not work well in conditions where random factors could influence traffic flows. Twitter is a valuable source of information as its users post events as they happen or shortly after. Therefore, Twitter data have been used to predict a wide variety of real-time outcomes. This paper aims to present a methodology for a real-time traffic event detection using Twitter. Tweets are obtained through the Twitter streaming application programming interface in real time with a geolocation filter. Then, the author used natural language processing techniques to process the tweets before they are fed into a text classification algorithm that identifies if it is traffic related or not. The authors implemented their methodology in the West Midlands region in the UK and obtained an overall accuracy of 92·86%

Incorporating travel time reliability in predicting the likelihood of severe crashes on arterial highways using non-parametric random-effect regression

Travel time reliability (TTR) modeling has gain attention among researchers\u27 due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time. Despite this significant effort, its impact on the severity of a crash is not well explored. This study analyzes the effect of TTR and other variables on the probability of the crash severity occurring on arterial roads. To address the unobserved heterogeneity problem, two random-effect regressions were applied; the Dirichlet random-effect (DRE) and the traditional random-effect (TRE) logistic regression. The difference between the two models is that the random-effect in the DRE is non-parametrically specified while in the TRE model is parametrically specified. The Markov Chain Monte Carlo simulations were adopted to infer the parameters\u27 posterior distributions of the two developed models. Using four-year police-reported crash data and travel speeds from Northeast Florida, the analysis of goodness-of-fit found the DRE model to best fit the data. Hence, it was used in studying the influence of TTR and other variables on crash severity. The DRE model findings suggest that TTR is statistically significant, at 95 percent credible intervals, influencing the severity level of a crash. A unit increases in TTR reduces the likelihood of a severe crash occurrence by 25 percent. Moreover, among the significant variables, alcohol/drug impairment was found to have the highest impact in influencing the occurrence of severe crashes. Other significant factors included traffic volume, weekends, speed, work-zone, land use, visibility, seatbelt usage, segment length, undivided/divided highway, and age

Testing the “Freight Landscape” Concept for Paris

International audienceThe concept of “freight landscape,” the basis for a modeling approach for urban freight traffic estimation using commonly available datasets, was proposed in 2017 with a case study applying it to the Los Angeles metropolitan area. To extend the scope of that research, we conduct another case study using data from the Paris region, France. We estimate spatial lag models using population, employment, or establishment transportation accessibilities as explanatory variables and network-based truck traffic as the dependent variable, modifying the approach used in the Los Angeles study. We identify differences in the characteristics of the variables and the models between the Los Angeles and Paris cases, each having a distinctively different urban structure. While the models estimated for the Paris region provide beneficial insights into the relationships between freight landscape indicators and urban freight traffic, the complex correlation structure among indicators, as well as the limitation of the models for specifying the areas of very high truck traffic, underlines the need for further research on the modeling framework and for more case studies