Towards a Better Understanding of Effectiveness of Bike-share Programs: Exploring Factors Affecting Bikes Idle Duration

Bike-share program is considered effective and reliable if its stations have bikes and empty docks available at any time of a day. Few studies have considered idle bikes in the system and even lesser have glanced on modeling bikes idle duration (BID) in the bike-share system. This study applied descriptive statistics and log-logistic hazard based model on one year Seattle bike-share ridership data to quantify the BID and determine factors associated with the bikes’ idle duration. The findings of the study illustrate that the most and least effective utilized bike were used for 161 hours and 0.19 hours respectively for the entire year. Winter season, especially when raining and snowing was found to increase the likelihood of long BID. On the other end, the bikes located in commercial areas were associated with short BID compared to residential land-use. Moreover, weekend days and evening peak hours (4 p.m. to 6 p.m.) are associated with less likelihood of the BID compared with weekdays and morning peak hours respectively. These findings will facilitate procedures to identify the idle bikes for redistribution strategy and enhancing effective utilization of the bike-share system

Safety Analysis Considering the Impactof Travel Time Reliability on Elderly Drivers

The main goal of this research was to evaluate how travel time reliability (TTR) might be associated with crashes involving elderly drivers, defined as those age 65 and above. Several TTR metrics were used to estimate their influence on elderly crash frequency and severity of the crash on freeways and arterial highways. The results suggest that TTR is statistically significant in affecting both elderly crash frequency and the severity of a crash involving an elderly driver. In particular, the analysis of risk ratios illustrates that a one-unit increase in the probability of congestion reduces the likelihood of the elderly severe crash by 22%

Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution

© 2017 Emmanuel Kidando et al. Multistate models, that is, models with more than two distributions, are preferred over single-state probability models in modeling the distribution of travel time. Literature review indicated that the finite multistate modeling of travel time using lognormal distribution is superior to other probability functions. In this study, we extend the finite multistate lognormal model of estimating the travel time distribution to unbounded lognormal distribution. In particular, a nonparametric Dirichlet Process Mixture Model (DPMM) with stick-breaking process representation was used. The strength of the DPMM is that it can choose the number of components dynamically as part of the algorithm during parameter estimation. To reduce computational complexity, the modeling process was limited to a maximum of six components. Then, the Markov Chain Monte Carlo (MCMC) sampling technique was employed to estimate the parameters’ posterior distribution. Speed data from nine links of a freeway corridor, aggregated on a 5-minute basis, were used to calculate the corridor travel time. The results demonstrated that this model offers significant flexibility in modeling to account for complex mixture distributions of the travel time without specifying the number of components. The DPMM modeling further revealed that freeway travel time is characterized by multistate or single-state models depending on the inclusion of onset and offset of congestion periods

Associating Pedestrian Crashes with Demographic and Socioeconomic Factors

© 2018 World Conference on Transport Research Society In the last decade, the concept of walkable neighborhoods has emerged as a topic of great interest. However, it is still unclear about the influence of socioeconomic and demographic factors on pedestrian crashes. This study proposed a methodology for pedestrian crash analysis that combines Geographic Information System (GIS) methods and statistical analysis to study the influence of socioeconomic and demographic factors on the occurrence of pedestrian crashes. The analysis was based on statewide crash data collected in Tennessee from 2008 to 2012. First, GIS kernel density technique was proposed to identify high concentration of pedestrian crash clusters and results were presented using cases studies of Davidson and Hamilton counties. GIS analysis identified pedestrian crash clusters among block groups with a high population who walk to work and block groups with a high number of housing units with no vehicles. A negative binomial model was applied using a statewide data to test the statistical significance of explanatory variables. As expected, model results indicated that population density, population from 15 to 64 years of age, high population of neighborhoods commuting to work by walking (without adequate facilities supporting pedestrians such as sidewalks and crosswalks) and high population of neighborhoods of housing units with no vehicles significantly increase the number of pedestrian crashes. However, blocks whose streets have adequate presence of median, shoulders, and sidewalks had negative coefficients hence their presence tends to decrease pedestrian crashes. Furthermore population commuting to work by private cars and high median household income significantly reduces pedestrian crash frequency. The findings from Kernel density and statistical modeling are relatively identical in the sense that all found household vehicle availability to be a factor in influencing frequency of pedestrian crashes. The findings of this study can assist in implementation of proactive pedestrian safety strategies

Bayesian Regression Approach to Estimate Speed Threshold under Uncertainty for Traffic Breakdown Event Identification

This study aims at developing a robust Bayesian statistical approach to determine the speed threshold (ST) for detecting a traffic breakdown event using traffic flow parameters. Data collected from a freeway section of I-295 in Jacksonville, Florida was used as a case study segment. The approach particularly is based on the change-point regression, in which two models - the Student-t and Gaussian residual distributed regressions - were developed and compared. The study found promising results in detecting the ST value when verified using the hypothesis test and simulated data. Moreover, it was found that the Student-t regression can significantly improve the goodness-of-fit compared with the Gaussian residual distributed regression. The methodology described in the current study can be used in the procedures of analyzing the breakdown process, stochastic roadway capacity analysis, congestion duration, the dynamic evolution of recurring traffic conditions, and clustering different traffic conditions. The results from these analyses provide useful information required in developing advanced traffic management strategies for highway operations

Evaluating the Service Life of Thermoplastic Pavement Markings: Stochastic Approach

© 2018 American Society of Civil Engineers. The study applied the Markov chain (MC) model that uses a transition matrix to transmit the probability of monitored pavement markings being in one service life state then changing into another service life state over a time interval. The service life prediction by MC models were then compared with those from linear models, testing if there were any clear advantages of using one model over the other in terms of predicting longevity of the marking retroreflectivity. The retroreflectivity data were collected by monitoring the coefficient of dry retroreflective luminance for 2 years using a handheld retroreflectometer. Using the MC model, the study found that the pavement marking retroreflectivity (PMR) degradation follows an exponential curve trend whereby the degradation rates decrease as the time increases. Significant differences were found in the deterioration of the markings based on the colors (white or yellow) and line type (center, lane line, or edge line). White thermoplastic edge lines on two-lane roadways were found to have a better performance (low deterioration rates) compared with the same lines on four-lane highways. Based on the transition probability matrix (TPM), it was observed that retroreflectivity is in an excellent or good state for a short period of time (54% probability) but is in a fair or poor state for a longer time (92%probability), suggesting the trend has a higher degradation rate at the beginning and a lower rate near the failure state. Keeping the minimum failure states at 150 and 100 mcd=m2=lx for white and yellow markings, respectively, the service life of white markings was found to be approximately 4 years (49.5 months) and it was found to be about 2.4 years (29 months) for yellow markings. The MC model findings were compared with those obtained through linear regression, which showed that white thermoplastic pavement markings take approximately 3.5 years (42 months) to deteriorate to failure state level, while yellow thermoplastics take about 2.1 years (25 months). The study concluded that there is a clear difference between the prediction using MC models compared with linear models, withMCmodels being more cost effective in terms of maintenance and replacement scheduling due to a longer life prediction

Examining the Influence of Alternative Fuels\u27 Regulations and Incentives on Electric-Vehicle Acquisition

States and federal administrations in America provide regulations and incentives to promote the utilization of alternative fuels. The contents and effects of such regulations and incentives have yet been explored to a great extent. This study evaluates the content and impact of the incentives and regulations on electric vehicle (EV) acquisitions using text mining and the negative binomial (NB) regression. Findings indicate that western states have a relatively higher number of EVs per million residents. Moreover, the NB results show that rebates and grants are associated with more EVs. On the other hand, exemptions and tax incentives are associated with lower EVs acquired. Loan incentives are associated with an increase in the acquisition of EVs but are statistically insignificant. Furthermore, air quality and emissions-related regulations are associated with the increased acquisition of EVs. The findings may assist agencies in identifying best practices and policies to promote alternative fuels

Evaluating Recurring Traffic Congestion Using Change Point Regression and Random Variation Markov Structured Model

© National Academy of Sciences: Transportation Research Board 2018. This study develops a probabilistic framework that evaluates the dynamic evolution of recurring traffic congestion (RTC) using the random variation Markov structured regression (MSR). This approach integrates the Markov chains assumption and probit regression. The analysis was performed using traffic data from a section of Interstate 295 located in Jacksonville, Florida. These data were aggregated on a 5-minute basis for 1 year (2015). Estimating discrete traffic states to apply the MSR model, this study established a definition of traffic congestion using Bayesian change point regression (BCR), in which the speed-occupancy relationship was explored. The MSR model with flow rate as a covariate was then used to estimate the probability of RTC occurrence. Findings from the BCR model suggest that the morning peak congested state occurs once speed is below 58 miles per hour (mph), whereas the evening peak period occurs at a speed below 55 mph. Evaluating the dynamics of traffic states over time, the Bayesian information criterion confirmed the hypothesis that a first-order Markov chain assumption is sufficient to characterize RTC. Moreover, the flow rate in the MSR model was found to be statistically significant in influencing the transition probability between the traffic regimes at 95% posterior credible interval. The knowledge of RTC transition explained by the approaches presented here will facilitate developing effective intervention strategies for mitigating RTC

Assessment of factors associated with travel time reliability and prediction: an empirical analysis using probabilistic reasoning approach

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence