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Mobile sensors are a useful data source with applications in several transportation fields. Though cost of collection, transmission, and storage has limited studies on driving data and safety, this can be overcome through usage-based insurance (UBI). In UBI programs, drivers are monitored, and their premiums are adjusted based on driver-level surrogate safety measures (SSMs) related to exposure and driving style. Contextual link-level SSMs (volume, speed, or density) could further improve discount calibration. This study quantifies relationships between contextual SSMs and crashes and includes the validation of previous results (correlations between SSMs and crashes and statistical models estimated using smartphone-collected data from Quebec City) and the comparison of three Canadian cities (using UBI data from Quebec City, Montreal, and Ottawa). Extracted SSMs were compared to large volumes of historical crash frequency data using Spearman’s Rank Correlation Coefficient and then implemented into spatial Bayesian crash models. Results from the UBI data generally matched those from the previous study, with observed correlations mirroring previous results in direction (braking, congestion, and speed variation are positively associated with crash frequency while mean speed is negatively associated) while correlation strength was slightly higher. Furthermore, these results were consistent between cities. For the crash modelling, repeatability of previous results in Quebec City was moderately good for the UBI data. Importantly for large-scale implementation, models estimated using UBI data were largely consistent between cities. This work provides an important contribution to the existing literature, clearly demonstrating how contextual safety measures could be applied to benefit UBI practices.
Joshua Stipancic; Etienne B. Racine; Aurélie Labbe; Nicolas Saunier; Luis Miranda-Moreno. Massive GNSS data for road safety analysis: Comparing crash models for several Canadian cities and data sources. Accident Analysis & Prevention 2021, 159, 106232 .
AMA StyleJoshua Stipancic, Etienne B. Racine, Aurélie Labbe, Nicolas Saunier, Luis Miranda-Moreno. Massive GNSS data for road safety analysis: Comparing crash models for several Canadian cities and data sources. Accident Analysis & Prevention. 2021; 159 ():106232.
Chicago/Turabian StyleJoshua Stipancic; Etienne B. Racine; Aurélie Labbe; Nicolas Saunier; Luis Miranda-Moreno. 2021. "Massive GNSS data for road safety analysis: Comparing crash models for several Canadian cities and data sources." Accident Analysis & Prevention 159, no. : 106232.
Pavement distresses, including cracking and disintegration, deteriorate road user’s comfort, damage vehicles, increase evasive maneuvers, and increase emissions. Transportation agencies spend a significant portion of their budget to monitor and maintain road pavements. Pavement distress can be identified through manual surveys, i.e., visual inspections of pavement images captured by an inspection vehicle. To reduce manual inspection costs, research and industry have moved quietly towards the development and implementation of automated road surface monitoring systems. Considering the latest research developments, the objective of this work is to propose and evaluate a methodology for automated detection and classification of pavement distress types using Convolutional Neural Networks (CNN) and a low-cost video data collection strategy. In this work, pavement distress types are categorized as linear or longitudinal crack, network crack, fatigue crack or pothole, patch, and pavement marking. The models are trained and tested based on an image dataset collected from Montreal’s road pavements. A sensitivity analysis is carried on for evaluating different regularization scenarios and data generation strategies especially scaling and partitioning the input image. The detection rate and classification accuracy of the proposed approach with the trained CNN model reaches 83.8% over the test set, which is promising compared with the literature. More specifically, the F1-scores for “pothole”, “patch”, “marking”, “crack-linear” and “crack-network” classes are 0.808, 0.802, 0.860, 0.796, and 0.813, respectively. However, by merging linear and network crack classes, the F1-score over the merged class increases to 0.916.
Ce Zhang; Ehsan Nateghinia; Luis F. Miranda-Moreno; Lijun Sun. Pavement distress detection using convolutional neural network (CNN): A case study in Montreal, Canada. International Journal of Transportation Science and Technology 2021, 1 .
AMA StyleCe Zhang, Ehsan Nateghinia, Luis F. Miranda-Moreno, Lijun Sun. Pavement distress detection using convolutional neural network (CNN): A case study in Montreal, Canada. International Journal of Transportation Science and Technology. 2021; ():1.
Chicago/Turabian StyleCe Zhang; Ehsan Nateghinia; Luis F. Miranda-Moreno; Lijun Sun. 2021. "Pavement distress detection using convolutional neural network (CNN): A case study in Montreal, Canada." International Journal of Transportation Science and Technology , no. : 1.
Introduction: Although stop signs are popular in North America, they have become controversial in cities like Montreal, Canada where they are often installed to reduce vehicular speeds and improve pedestrian safety despite limited evidence demonstrating their effectiveness. The purpose of this study is to evaluate the impact of stop-control configuration (and other features) on safety using statistical models and surrogate measures of safety (SMoS), namely vehicle speed, time-to-collision (TTC), and post-encroachment time (PET), while controlling for features of traffic, geometry, and built environment. Methods: This project leverages high-resolution user trajectories extracted from video data collected for 100 intersections, 336 approaches, and 130,000 road users in Montreal to develop linear mixed-effects regression models to account for within-site and within-approach correlations. This research proposes the Intersection Exposure Group (IEG) indicator, an original method for classifying microscopic exposure of pedestrians and vehicles. Results: Stop signs were associated with an average decrease in approach speed of 17.2 km/h and 20.1 km/h, at partially and fully stop-controlled respectively. Cyclist or pedestrian presence also significantly lower vehicle speeds. The proposed IEG measure was shown to successfully distinguish various types of pedestrian-vehicle interactions, allowing for the effect of each interaction type to vary in the model. Conclusions: The presence of stop signs significantly reduced approach speeds compared to uncontrolled approaches. Though several covariates were significantly related to TTC and PET for vehicle pairs, the models were unable to demonstrate a significant relationship between stop signs and vehicle–pedestrian interactions. Therefore, drawing conclusions regarding pedestrian safety is difficult. Practical Applications: As pedestrian safety is frequently used to justify new stop sign installations, this result has important policy implications. Policies implementing stop signs to reduce pedestrian crashes may be less effective than other interventions. Enforcement and education efforts, along with geometric design considerations, should accompany any changes in traffic control.
Joshua Stipancic; Paul G. St-Aubin; Bismarck Ledezma-Navarro; Aurélie Labbe; Nicolas Saunier; Luis Miranda-Moreno. Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis. Journal of Safety Research 2021, 77, 311 -323.
AMA StyleJoshua Stipancic, Paul G. St-Aubin, Bismarck Ledezma-Navarro, Aurélie Labbe, Nicolas Saunier, Luis Miranda-Moreno. Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis. Journal of Safety Research. 2021; 77 ():311-323.
Chicago/Turabian StyleJoshua Stipancic; Paul G. St-Aubin; Bismarck Ledezma-Navarro; Aurélie Labbe; Nicolas Saunier; Luis Miranda-Moreno. 2021. "Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis." Journal of Safety Research 77, no. : 311-323.
Governments around the world have implemented measures to slow down the spread of COVID-19, resulting in a substantial decrease in the usage of motorized transportation. The ensuing decrease in the emission of traffic-related heat and pollutants is expected to impact the environment through various pathways, especially near urban areas, where there is a higher concentration of traffic. In this study, we perform high-resolution urban climate simulations to assess the direct impact of the decrease in traffic-related heat emissions due to COVID-19 on urban temperature characteristics. One simulation spans the January–May 2020 period; two additional simulations spanning the April 2019–May 2020 period, with normal and reduced traffic, are used to assess the impacts throughout the year. These simulations are performed for the city of Montreal, the second largest urban centre in Canada. The mechanisms and main findings of this study are likely to be applicable to most large urban centres around the globe. The results show that an 80% reduction in traffic results in a decrease of up to 1 °C in the near-surface temperature for regions with heavy traffic. The magnitude of the temperature decrease varies substantially with the diurnal traffic cycle and also from day to day, being greatest when the near-surface wind speeds are low and there is a temperature inversion in the surface layer. This reduction in near-surface temperature is reflected by an up to 20% reduction in hot hours (when temperature exceeds 30 °C) during the warm season, thus reducing heat stress for vulnerable populations. No substantial changes occur outside of traffic corridors, indicating that potential reductions in traffic would need to be supplemented by additional measures to reduce urban temperatures and associated heat stress, especially in a warming climate, to ensure human health and well-being.
Bernardo Teufel; Laxmi Sushama; Vincent Poitras; Tarek Dukhan; Stéphane Bélair; Luis Miranda-Moreno; Lijun Sun; Agus Sasmito; Girma Bitsuamlak. Impact of COVID-19-Related Traffic Slowdown on Urban Heat Characteristics. Atmosphere 2021, 12, 243 .
AMA StyleBernardo Teufel, Laxmi Sushama, Vincent Poitras, Tarek Dukhan, Stéphane Bélair, Luis Miranda-Moreno, Lijun Sun, Agus Sasmito, Girma Bitsuamlak. Impact of COVID-19-Related Traffic Slowdown on Urban Heat Characteristics. Atmosphere. 2021; 12 (2):243.
Chicago/Turabian StyleBernardo Teufel; Laxmi Sushama; Vincent Poitras; Tarek Dukhan; Stéphane Bélair; Luis Miranda-Moreno; Lijun Sun; Agus Sasmito; Girma Bitsuamlak. 2021. "Impact of COVID-19-Related Traffic Slowdown on Urban Heat Characteristics." Atmosphere 12, no. 2: 243.
Many of the existing studies on vehicular fuel consumption estimation are criticized in aspects such as ignoring real-world training data, low diversity of test fleet, impracticality of models in real-world applications (i.e. instrument-independent eco-driving), or their prediction power in the non-linear multi-dimensional space of fuel consumption estimation. In this paper, we proposed a machine learning modeling method using large on-road data collected from a fleet of 27 vehicles. The usability of models in absence of specialized instruments was in focus. We tried to improve the accuracy of our base models by introducing engine-speed estimates through a cascaded modeling procedure. As a result, the accuracy of models reached 83%, while improvements as high as 37% were achieved depending on the technique (support vector regression or artificial neural networks) and vehicle class. Finally, we took the first step from vehicle-specific models towards category-specific modeling by a categorical analysis over fleet attributes.
Ehsan Moradi; Luis Miranda-Moreno. Vehicular fuel consumption estimation using real-world measures through cascaded machine learning modeling. Transportation Research Part D: Transport and Environment 2020, 88, 102576 .
AMA StyleEhsan Moradi, Luis Miranda-Moreno. Vehicular fuel consumption estimation using real-world measures through cascaded machine learning modeling. Transportation Research Part D: Transport and Environment. 2020; 88 ():102576.
Chicago/Turabian StyleEhsan Moradi; Luis Miranda-Moreno. 2020. "Vehicular fuel consumption estimation using real-world measures through cascaded machine learning modeling." Transportation Research Part D: Transport and Environment 88, no. : 102576.
Planning for accessibility is increasingly considered in the development of equitable plans by transport agencies and it has also been shown to exert a positive influence on public transport use. However, this influence has not been examined across income groups and in different geographic regions of varying sizes. The present study measures the relationship between accessibility and mode choice for low- and higher-income groups in eleven Canadian metropolitan regions. Our results show that the impact of accessibility on public transport mode share is stronger and non-linear for the low-income group especially in the largest metropolitan areas, where increasing accessibility past a certain optimal value will lead to a decrease in public transport mode share. However, this point occurs at the 80th percentile of existing accessibility, so improvements in mode share are nonetheless expected with improved accessibility in most areas within these regions. Moreover, in regions where an optimal value is not readily observed, improved accessibility throughout the region would lead to increased uptake of public transport for both the higher- and to a greater extent, the low-income group. Findings from this paper can be of value to transport professionals working towards meeting ridership goals around the world as comparisons between groups and across regions highlight the variation in the impacts of accessibility on mode share.
Boer Cui; Geneviève Boisjoly; Luis Miranda-Moreno; Ahmed El-Geneidy. Accessibility matters: Exploring the determinants of public transport mode share across income groups in Canadian cities. Transportation Research Part D: Transport and Environment 2020, 80, 102276 .
AMA StyleBoer Cui, Geneviève Boisjoly, Luis Miranda-Moreno, Ahmed El-Geneidy. Accessibility matters: Exploring the determinants of public transport mode share across income groups in Canadian cities. Transportation Research Part D: Transport and Environment. 2020; 80 ():102276.
Chicago/Turabian StyleBoer Cui; Geneviève Boisjoly; Luis Miranda-Moreno; Ahmed El-Geneidy. 2020. "Accessibility matters: Exploring the determinants of public transport mode share across income groups in Canadian cities." Transportation Research Part D: Transport and Environment 80, no. : 102276.
Automated monitoring of pedestrians on non-motorized facilities with high pedestrian flows is challenging. Several automated sensor solutions are commercially available that have been evaluated in the literature including traditional point-based sensors, such as inductive loop detectors for bicycles and infrared sensors for pedestrians. More recently, image-based systems, based on video cameras or thermal video cameras, have been developed. Despite the various options, some key limitations of existing solutions exist, in particular, the lack of low-cost solutions using embedded systems capable of performing in real-time under high volume (flow) conditions. This work aims at developing and evaluating the performance of a novel, real-time counting system, developed for environments with high pedestrian flows. The proposed system is based on emerging LiDAR (Light Detection and Ranging) technology. As an input, the system uses the distance measurements from a two-dimensional LiDAR sensor with a set of distinct laser channels and a given angular resolution between each channel. The developed system processes those measurements using a clustering algorithm to detect, count, and identify the direction of travel of each pedestrian. The system’s performance is evaluated by comparing its directional counting outputs with manual counts (ground truth) using disaggregate and aggregate (15-minutes interval) counts at two different monitoring locations. The results demonstrate that the system accurately counts more than 97% of the pedestrians at the disaggregate level, with a false direction detection rate of 1.1%. The over-counting error is 0.7% and the under-counting errors are 1.3% and 2.7% for the two selected sites. At the aggregate level (15-minutes interval), the average absolute percentage deviations (AAPDs) are 1.6% and 4.3% while the weighted AAPDs are 1.5% and 3.5% for the first and second sites, respectively. The accuracy of the proposed system is higher than the traditional technologies used for the same purpose.
Asad Lesani; Ehsan Nateghinia; Luis F. Miranda-Moreno. Development and evaluation of a real-time pedestrian counting system for high-volume conditions based on 2D LiDAR. Transportation Research Part C: Emerging Technologies 2020, 114, 20 -35.
AMA StyleAsad Lesani, Ehsan Nateghinia, Luis F. Miranda-Moreno. Development and evaluation of a real-time pedestrian counting system for high-volume conditions based on 2D LiDAR. Transportation Research Part C: Emerging Technologies. 2020; 114 ():20-35.
Chicago/Turabian StyleAsad Lesani; Ehsan Nateghinia; Luis F. Miranda-Moreno. 2020. "Development and evaluation of a real-time pedestrian counting system for high-volume conditions based on 2D LiDAR." Transportation Research Part C: Emerging Technologies 114, no. : 20-35.
Pedestrian safety in proximity to schools is a major concern of transportation authorities, local governments, and residents. In fact, several countermeasures (e.g., school-zone speed limits) are usually in place around schools to provide a safer environment, especially for school-age children. Two questions arise here: (i) are transportation facilities in proximity to schools truly safer than other facilities given a variety of implemented road safety interventions around schools? and (ii) how can we answer the previous question properly using a reliable approach that accounts for possible confounding? While previous literature has mixed results and does not provide clear methodological/empirical guidelines in this regard, we propose an approach that answers the above questions. We illustrate our method on a sample of intersections in Montreal, Canada. Specifically, to underpin a causal interpretation, for the first time in the extent of transportation literature, we develop a heterogeneous endogenous econometric model that estimates the causal effect of proximity to school on pedestrian safety, addressing a complex endogenous relationship between the two. Various built environment, traffic exposure, and road geometric/operational characteristics are considered. The results indicate that if endogeneity is not accounted for, the effect of proximity to school is underestimated, while not being significant at a 5% level of significance. However, after accounting for confounding factors, the proposed endogenous model indicates that proximity to school deteriorates pedestrian safety. Therefore, traffic safety countermeasures and policies in place (if any) during the study period have not been sufficient and/or effective in improving pedestrian safety at intersections near schools. Our heterogeneity in mean and variance formulation provided more insights. For example, we found that, interestingly, as pedestrian volume increases at intersections around schools, the adverse effect of proximity to school on pedestrian safety decreases, a possibility not previously explored in the extent of road safety literature, confirming a strong safety-in-numbers effect.
Shahram Heydari; Luis Miranda-Moreno; Adrian J. Hickford. On the causal effect of proximity to school on pedestrian safety at signalized intersections: A heterogeneous endogenous econometric model. Analytic Methods in Accident Research 2020, 26, 100115 .
AMA StyleShahram Heydari, Luis Miranda-Moreno, Adrian J. Hickford. On the causal effect of proximity to school on pedestrian safety at signalized intersections: A heterogeneous endogenous econometric model. Analytic Methods in Accident Research. 2020; 26 ():100115.
Chicago/Turabian StyleShahram Heydari; Luis Miranda-Moreno; Adrian J. Hickford. 2020. "On the causal effect of proximity to school on pedestrian safety at signalized intersections: A heterogeneous endogenous econometric model." Analytic Methods in Accident Research 26, no. : 100115.
Intersections represent the most dangerous sites in the road network for pedestrians: not only is modal separation often impossible, but elements of geometry, traffic control, and built environment further exacerbate crash risk. Evaluating the safety impact of intersection features requires methods to quantify relationships between different factors and pedestrian injuries. The purpose of this paper is to model the effects of exposure, geometry, and signalization on pedestrian injuries at urban signalized intersections using a Full Bayes spatial Poisson Log-Normal model that accounts for unobserved heterogeneity and spatial correlation. Using the Integrated Nested Laplace Approximation (INLA) technique, this work leverages a rich database of geometric and signalization variables for 1864 intersections in Montreal, Quebec. To collect exposure data, short-term pedestrian and vehicle counts were extrapolated to AADT using developed expansion factors. Results of the model confirmed the positive relationship between pedestrian and vehicle volumes and pedestrian injuries. Curb extensions, raised medians, and exclusive left turn lanes were all found to reduce pedestrian injuries, while the total number of lanes and the number of commercial entrances were found to increase them. Pedestrian priority phases reduced injuries while the green straight arrow increased injuries. Lastly, the posterior expected number of crashes was used to identify hotspots. The proposed ranking criteria identified many intersections close to the city centre where the expected number of crashes is highest and intersections along arterials with lower pedestrian volumes where individual pedestrian risk is elevated. Understanding the effects of intersection geometry and pedestrian signalization will aid in ensuring the safety of pedestrians at signalized intersections.
Joshua Stipancic; Luis Miranda-Moreno; Jillian Strauss; Aurélie Labbe. Pedestrian safety at signalized intersections: Modelling spatial effects of exposure, geometry and signalization on a large urban network. Accident Analysis & Prevention 2019, 134, 105265 .
AMA StyleJoshua Stipancic, Luis Miranda-Moreno, Jillian Strauss, Aurélie Labbe. Pedestrian safety at signalized intersections: Modelling spatial effects of exposure, geometry and signalization on a large urban network. Accident Analysis & Prevention. 2019; 134 ():105265.
Chicago/Turabian StyleJoshua Stipancic; Luis Miranda-Moreno; Jillian Strauss; Aurélie Labbe. 2019. "Pedestrian safety at signalized intersections: Modelling spatial effects of exposure, geometry and signalization on a large urban network." Accident Analysis & Prevention 134, no. : 105265.
The cycling safety research literature has proposed methods to analyse safety and case studies to better understand the factors that lead to cyclist crashes. Surrogate measures of safety (SMoS) are being used as a proactive approach to identify severe interactions that do not result in an accident and interpreting them for a safety diagnosis. While most cyclist studies adopting SMoS have evaluated interactions by counting the total number of severe events per location, only a few have focused on the interactions between general directions of movement e.g. through cyclists and right turning vehicles. However, road users perform maneuvers that are more varied at a high spatiotemporal resolution such as a range of sharp to wide turning movements. These maneuvers (motion patterns) have not been considered in past studies as a basis for analysis to identify, among a range of possible motion patterns in each direction of travel, which ones are safer, and which are more likely to result in a crash. This paper presents a novel movement-based probabilistic SMoS approach to evaluate the safety of road users' trajectories based on clusters of trajectories representing the various movements. This approach is applied to cyclist-vehicle interactions at two locations of cycling network discontinuity and two control sites in Montréal. The Kruskal-Wallis and Kolmogorov-Smirnov tests are used to compare the time-to-collision (TTC) distribution between motion patterns in each site and between sites with and without a discontinuity. Results demonstrate the insight provided by the new approach and indicate that cyclist interactions are more severe and less safe at locations with a cycling network discontinuity and that cyclists following different movements have statistically different levels of safety.
Matin S. Nabavi Niaki; Nicolas Saunier; Luis F. Miranda-Moreno. Is that move safe? Case study of cyclist movements at intersections with cycling discontinuities. Accident Analysis & Prevention 2019, 131, 239 -247.
AMA StyleMatin S. Nabavi Niaki, Nicolas Saunier, Luis F. Miranda-Moreno. Is that move safe? Case study of cyclist movements at intersections with cycling discontinuities. Accident Analysis & Prevention. 2019; 131 ():239-247.
Chicago/Turabian StyleMatin S. Nabavi Niaki; Nicolas Saunier; Luis F. Miranda-Moreno. 2019. "Is that move safe? Case study of cyclist movements at intersections with cycling discontinuities." Accident Analysis & Prevention 131, no. : 239-247.
Due to a lack of reliable data collection systems, traffic fatalities and injuries are often under-reported in developing countries. Recent developments in surrogate road safety methods and video analytics tools offer an alternative approach that can be both lower cost and more time efficient when crash data is incomplete or missing. However, very few studies investigating pedestrian road safety in developing countries using these approaches exist. This research uses an automated video analytics tool to develop and analyze surrogate traffic safety measures and to evaluate the effectiveness of temporary low-cost countermeasures at selected pedestrian crossings at risky intersections in the city of Cochabamba, Bolivia. Specialized computer vision software is used to process hundreds of hours of video data and generate data on road users’ speed and trajectories. We find that motorcycles, turning movements, and roundabouts, are among the key factors related to pedestrian crash risk, and that the implemented treatments were effective at four-legged intersections but not at traditional-design roundabouts. This study demonstrates the applicability of the surrogate methodology based on automated video analytics in the Latin American context, where traditional methods are challenging to implement. The methodology could serve as a tool to rapidly evaluate temporary treatments before they are permanently implemented and replicated.
Lynn Scholl; Mohamed Elagaty; Bismarck Ledezma-Navarro; Edgar Zamora; Luis Miranda-Moreno. A Surrogate Video-Based Safety Methodology for Diagnosis and Evaluation of Low-Cost Pedestrian-Safety Countermeasures: The Case of Cochabamba, Bolivia. Sustainability 2019, 11, 4737 .
AMA StyleLynn Scholl, Mohamed Elagaty, Bismarck Ledezma-Navarro, Edgar Zamora, Luis Miranda-Moreno. A Surrogate Video-Based Safety Methodology for Diagnosis and Evaluation of Low-Cost Pedestrian-Safety Countermeasures: The Case of Cochabamba, Bolivia. Sustainability. 2019; 11 (17):4737.
Chicago/Turabian StyleLynn Scholl; Mohamed Elagaty; Bismarck Ledezma-Navarro; Edgar Zamora; Luis Miranda-Moreno. 2019. "A Surrogate Video-Based Safety Methodology for Diagnosis and Evaluation of Low-Cost Pedestrian-Safety Countermeasures: The Case of Cochabamba, Bolivia." Sustainability 11, no. 17: 4737.
Vision-based trajectory data provides great details for investigating microscopic behavior and road safety at the level of interactions. Most studies investigating pedestrian-vehicle interactions at non-signalized intersections have focused on interactions at the crosswalk on the same approach where the vehicle is coming from, which are referred to as primary interactions in this study. However, secondary interactions, defined as interactions between vehicles exiting the intersection and crossing pedestrians, have not been adequately studied. Second interactions can pose more dangers to pedestrians due to the driver’s unclear knowledge of right-of-way, acceleration attempts to recover the speed, and the complex situation the driver faces in the intersection. This paper aims at studying the safety issue of secondary pedestrian-vehicle interactions at non-signalized intersections. For that purpose, a methodology is proposed based on surrogate measures of safety and behavioral measures derived from vision-based trajectories. This methodology is implemented through a case study involving ten all-way stop intersections from Montreal, Canada. Road user trajectory data are automatically extracted from the videos. Different measures are used in the study: from the interaction analysis that determines vehicle-pedestrian interactions based on a Distance-Velocity (DV) model, average crossing speeds and vehicle approaching behaviors in terms of speed and acceleration. Computer tools are developed to extract these measures from the trajectory data. Based on these measures, a comparative analysis is carried out between primary and secondary interactions. Results show that secondary interactions are more dangerous than primary interactions. Among the three secondary interaction types, secondary through interactions are the most dangerous.
Ting Fu; Weichao Hu; Luis Miranda-Moreno; Nicolas Saunier. Investigating secondary pedestrian-vehicle interactions at non-signalized intersections using vision-based trajectory data. Transportation Research Part C: Emerging Technologies 2019, 105, 222 -240.
AMA StyleTing Fu, Weichao Hu, Luis Miranda-Moreno, Nicolas Saunier. Investigating secondary pedestrian-vehicle interactions at non-signalized intersections using vision-based trajectory data. Transportation Research Part C: Emerging Technologies. 2019; 105 ():222-240.
Chicago/Turabian StyleTing Fu; Weichao Hu; Luis Miranda-Moreno; Nicolas Saunier. 2019. "Investigating secondary pedestrian-vehicle interactions at non-signalized intersections using vision-based trajectory data." Transportation Research Part C: Emerging Technologies 105, no. : 222-240.
Crash frequency and injury severity are independent dimensions defining crash risk in road safety management and network screening. Traditional screening techniques model crashes using regression and historical crash data, making them intrinsically reactive. In response, surrogate measures of safety have become a popular proactive alternative. The purpose of this paper is to develop models for crash frequency and severity incorporating GPS-derived surrogate safety measures (SSMs) as predictive variables. SSMs based on vehicle manoeuvres and traffic flow were extracted from data collected in Quebec City. The mixed multivariate outcome is estimated using two models; a Full Bayes Spatial Negative Binomial model for crash frequency estimated using the Integrated Nested Laplace Approximation approach and a fractional Multinomial Logit model for crash severity. Model outcomes are combined to generate posterior expected crash frequency at each severity level and rank sites based on crash cost. The crash frequency model was accurate at the network scale, with the majority of proposed SSMs statistically significant at 95% confidence and the direction of their effect generally consistent with previous research. In the crash severity model, fewer variables were significant, yet the direction of the effect of all significant variables was again consistent with previous research. Correlations between rankings predicted by the mixed multivariate model and by the crash data were adequate for intersections (0.46) but were poorer for links (0.25). The ability to prioritize sites based on GPS data and SSMs rather than historical crash data represents a substantial contribution to the field of road safety.
Joshua Stipancic; Luis Miranda-Moreno; Nicolas Saunier; Aurélie Labbe. Network screening for large urban road networks: Using GPS data and surrogate measures to model crash frequency and severity. Accident Analysis & Prevention 2019, 125, 290 -301.
AMA StyleJoshua Stipancic, Luis Miranda-Moreno, Nicolas Saunier, Aurélie Labbe. Network screening for large urban road networks: Using GPS data and surrogate measures to model crash frequency and severity. Accident Analysis & Prevention. 2019; 125 ():290-301.
Chicago/Turabian StyleJoshua Stipancic; Luis Miranda-Moreno; Nicolas Saunier; Aurélie Labbe. 2019. "Network screening for large urban road networks: Using GPS data and surrogate measures to model crash frequency and severity." Accident Analysis & Prevention 125, no. : 290-301.
In the past decade, transportation planners worldwide have been incorporating shared space design elements as a way of creating pedestrian-friendly places. Streets incorporating shared-space principles tend to have reduced vehicle speeds and increased safety for vulnerable road users. In North American cities, a shared-space approach is rarely applied to non-motorized environments such as pedestrian malls, campuses, and parks. As cyclists and pedestrians travel at relatively slow speeds, there is an opportunity to provide safe infrastructure to both through non-motorized shared spaces. Yet, little empirical evidence exists concerning the risk of pedestrian-cyclist collisions in shared spaces. Existing surrogate methods are either difficult to automate or insufficient to describe interactions between vulnerable users. To address this research gap, a methodological framework is proposed based on the analysis of semi-automated pedestrian-cyclist interactions and the integration of surrogate methods in non-motorized shared space. Several proposed surrogate safety measures (SSMs) including cyclist speed, angle of approach, pedestrian density, and post-encroachment time are analysed to estimate the risk of pedestrian-cyclist interactions. The methodology is then applied to a case study on the McGill University campus in Montreal, Canada, where cyclists and pedestrians coexist. User trajectories are automatically extracted using a computer vision software to yield 2739 pedestrian-cyclist interactions for analysis. The derived SSMs demonstrate adequate levels of safety. For example, speed and pedestrian density are shown to be negatively correlated, while conflict rate and density are positively correlated. Statistical differences are shown between conflict types defined based on intersecting angle and road user configuration.
David Beitel; Joshua Stipancic; Kevin Manaugh; Luis Miranda-Moreno. Assessing safety of shared space using cyclist-pedestrian interactions and automated video conflict analysis. Transportation Research Part D: Transport and Environment 2018, 65, 710 -724.
AMA StyleDavid Beitel, Joshua Stipancic, Kevin Manaugh, Luis Miranda-Moreno. Assessing safety of shared space using cyclist-pedestrian interactions and automated video conflict analysis. Transportation Research Part D: Transport and Environment. 2018; 65 ():710-724.
Chicago/Turabian StyleDavid Beitel; Joshua Stipancic; Kevin Manaugh; Luis Miranda-Moreno. 2018. "Assessing safety of shared space using cyclist-pedestrian interactions and automated video conflict analysis." Transportation Research Part D: Transport and Environment 65, no. : 710-724.
A real-time pedestrian monitoring system provides information about traffic flow, speeds, travel times, and time spent in areas or transportation facilities of interest. This is useful in travel information systems and crowd management strategies, as well as in planning and emergencies in public spaces, such as airports, parks, malls, and university campuses. While there are technologies that can obtain count data for non-motorized transportation at specific locations, most technologies cannot provide origin-destination information, trip paths, travel times, or time spent. To overcome these shortcomings, some studies have explored the use of Bluetooth (BT) sensors to capture the unique media access control (MAC) addresses of mobile devices carried by pedestrians. However, this collection method may suffer from low-detection rates. As an alternative, collecting MAC data from WiFi signals has emerged. The objective of this paper is three-fold: 1) develop and evaluate the performance of an integrated WiFi-BT system to monitor pedestrian-cyclists activity traffic; 2) develop and validate a classification method for differentiating pedestrians from bicycles; and 3) propose a simple extrapolation method that combines counts and MAC data. Among other results, relatively high detection rates were obtained for the developed WiFi system in comparison with BT sensors. In addition, high correlation between estimated and ground truth speeds and low classification errors are observed. Finally, the extrapolated WiFi counts and ground truth counts were found to be highly correlated. These results demonstrate the feasibility of the proposed system and methods to estimate travel times (speeds), to classify bicycle-pedestrian WiFi signals, and to extrapolate pedestrian MAC counts.
Asad Lesani; Luis Miranda-Moreno. Development and Testing of a Real-Time WiFi-Bluetooth System for Pedestrian Network Monitoring, Classification, and Data Extrapolation. IEEE Transactions on Intelligent Transportation Systems 2018, 20, 1484 -1496.
AMA StyleAsad Lesani, Luis Miranda-Moreno. Development and Testing of a Real-Time WiFi-Bluetooth System for Pedestrian Network Monitoring, Classification, and Data Extrapolation. IEEE Transactions on Intelligent Transportation Systems. 2018; 20 (4):1484-1496.
Chicago/Turabian StyleAsad Lesani; Luis Miranda-Moreno. 2018. "Development and Testing of a Real-Time WiFi-Bluetooth System for Pedestrian Network Monitoring, Classification, and Data Extrapolation." IEEE Transactions on Intelligent Transportation Systems 20, no. 4: 1484-1496.
Improving road safety requires accurate network screening methods to identify and prioritize sites in order to maximize the effectiveness of implemented countermeasures. In screening, hotspots are commonly identified using statistical models and ranking criteria derived from observed crash data. However, collision databases are subject to errors, omissions, and underreporting. More importantly, crash-based methods are reactive and require years of crash data. With the arrival of new technologies including Global Positioning System (GPS) trajectory data, proactive surrogate safety methods have gained popularity as an alternative approach for screening. GPS-enabled smartphones can collect reliable and spatio-temporally rich driving data from regular drivers using an inexpensive, simple, and user-friendly tool. However, few studies to date have analyzed large volumes of smartphone GPS data and considered surrogate-safety modelling techniques for network screening. The purpose of this paper is to propose a surrogate safety screening approach based on smartphone GPS data and a Full Bayesian modelling framework. After processing crash data and GPS data collected in Quebec City, Canada, several surrogate safety measures (SSMs), including vehicle manoeuvres (hard braking) and measures of traffic flow (congestion, average speed, and speed variation), were extracted. Then, spatial crash frequency models incorporating the extracted SSMs were proposed and validated. A Latent Gaussian Spatial Model was estimated using the Integrated Nested Laplace Approximation (INLA) technique. While the INLA Negative Binomial models outperformed alternative models, incorporating spatial correlations provided the greatest improvement in model fit. Relationships between SSMs and crash frequency established in previous studies were generally supported by the modelling results. For example, hard braking, congestion, and speed variation were all positively linked to crash counts at the intersection level. Network screening based on SSMs presents a substantial contribution to the field of road safety and works towards the elimination of crash data in evaluation and monitoring.
Joshua Stipancic; Luis Miranda-Moreno; Nicolas Saunier; Aurelie Labbe. Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent Gaussian Spatial Models. Accident Analysis & Prevention 2018, 120, 174 -187.
AMA StyleJoshua Stipancic, Luis Miranda-Moreno, Nicolas Saunier, Aurelie Labbe. Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent Gaussian Spatial Models. Accident Analysis & Prevention. 2018; 120 ():174-187.
Chicago/Turabian StyleJoshua Stipancic; Luis Miranda-Moreno; Nicolas Saunier; Aurelie Labbe. 2018. "Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent Gaussian Spatial Models." Accident Analysis & Prevention 120, no. : 174-187.
The primary purpose of any transportation network is to provide connectivity between the origin and travel destination. However, given the vehicle oriented structure of the road network in many countries, there are connectivity issues in the c...
Matin S. Nabavi Niaki; Nicolas Saunier; Luis F. Miranda-Moreno. Analysing cyclist behaviour at cycling facility discontinuities using video data. Transactions on Transport Sciences 2018, 9, 3 -17.
AMA StyleMatin S. Nabavi Niaki, Nicolas Saunier, Luis F. Miranda-Moreno. Analysing cyclist behaviour at cycling facility discontinuities using video data. Transactions on Transport Sciences. 2018; 9 (1):3-17.
Chicago/Turabian StyleMatin S. Nabavi Niaki; Nicolas Saunier; Luis F. Miranda-Moreno. 2018. "Analysing cyclist behaviour at cycling facility discontinuities using video data." Transactions on Transport Sciences 9, no. 1: 3-17.
Network screening is a key element in identifying and prioritizing hazardous sites for engineering treatment. Traditional screening methods have used observed crash frequency or severity ranking criteria and statistical modelling approaches, despite the fact that crash-based methods are reactive. Alternatively, surrogate safety measures (SSMs) have become popular, making use of new data sources including video and, more rarely, GPS data. The purpose of this study is to examine vehicle manoeuvres of braking and accelerating extracted from a large quantity of GPS data collected using the smartphones of regular drivers, and to explore their potential as SSMs through correlation with historical collision frequency and severity across different facility types. GPS travel data was collected in Quebec City, Canada in 2014. The sample for this study contained over 4000 drivers and 21,000 trips. Hard braking (HBEs) and accelerating events (HAEs) were extracted and compared to historical crash data using Spearman’s correlation coefficient and pairwise Kolmogorov-Smirnov tests. Both manoeuvres were shown to be positively correlated with crash frequency at the link and intersection levels, though correlations were much stronger when considering intersections. Locations with more braking and accelerating also tend to have more collisions. Concerning severity, higher numbers of vehicle manoeuvres were also related to increased collision severity, though this relationship was not always statistically significant. The inclusion of severity testing, which is an independent dimension of safety, represents a substantial contribution to the existing literature. Future work will focus on developing a network screening model that incorporates these SSMs.
Joshua Stipancic; Luis Miranda-Moreno; Nicolas Saunier. Vehicle manoeuvers as surrogate safety measures: Extracting data from the gps-enabled smartphones of regular drivers. Accident Analysis & Prevention 2018, 115, 160 -169.
AMA StyleJoshua Stipancic, Luis Miranda-Moreno, Nicolas Saunier. Vehicle manoeuvers as surrogate safety measures: Extracting data from the gps-enabled smartphones of regular drivers. Accident Analysis & Prevention. 2018; 115 ():160-169.
Chicago/Turabian StyleJoshua Stipancic; Luis Miranda-Moreno; Nicolas Saunier. 2018. "Vehicle manoeuvers as surrogate safety measures: Extracting data from the gps-enabled smartphones of regular drivers." Accident Analysis & Prevention 115, no. : 160-169.
This paper proposes a new framework to evaluate pedestrian safety at non-signalized crosswalk locations. In the proposed framework, the yielding maneuver of a driver in response to a pedestrian is split into the reaction and braking time. Hence, the relationship of the distance required for a yielding maneuver and the approaching vehicle speed depends on the reaction time of the driver and deceleration rate that the vehicle can achieve. The proposed framework is represented in the distance-velocity (DV) diagram and referred as the DV model. The interactions between approaching vehicles and pedestrians showing the intention to cross are divided in three categories: i) situations where the vehicle cannot make a complete stop, ii) situations where the vehicle's ability to stop depends on the driver reaction time, and iii) situations where the vehicle can make a complete stop. Based on these classifications, non-yielding maneuvers are classified as "non-infraction non-yielding" maneuvers, "uncertain non-yielding" maneuvers and "non-yielding" violations, respectively. From the pedestrian perspective, crossing decisions are classified as dangerous crossings, risky crossings and safe crossings accordingly. The yielding compliance and yielding rate, as measures of the yielding behavior, are redefined based on these categories. Time to crossing and deceleration rate required for the vehicle to stop are used to measure the probability of collision. Finally, the framework is demonstrated through a case study in evaluating pedestrian safety at three different types of non-signalized crossings: a painted crosswalk, an unprotected crosswalk, and a crosswalk controlled by stop signs. Results from the case study suggest that the proposed framework works well in describing pedestrian-vehicle interactions which helps in evaluating pedestrian safety at non-signalized crosswalk locations.
Ting Fu; Luis Miranda-Moreno; Nicolas Saunier. A novel framework to evaluate pedestrian safety at non-signalized locations. Accident Analysis & Prevention 2018, 111, 23 -33.
AMA StyleTing Fu, Luis Miranda-Moreno, Nicolas Saunier. A novel framework to evaluate pedestrian safety at non-signalized locations. Accident Analysis & Prevention. 2018; 111 ():23-33.
Chicago/Turabian StyleTing Fu; Luis Miranda-Moreno; Nicolas Saunier. 2018. "A novel framework to evaluate pedestrian safety at non-signalized locations." Accident Analysis & Prevention 111, no. : 23-33.
Philippe Barla; Mathieu Gilbert-Gonthier; Marco Antonio Lopez Castro; Luis Miranda-Moreno. Eco-driving training and fuel consumption: Impact, heterogeneity and sustainability. Energy Economics 2017, 62, 187 -194.
AMA StylePhilippe Barla, Mathieu Gilbert-Gonthier, Marco Antonio Lopez Castro, Luis Miranda-Moreno. Eco-driving training and fuel consumption: Impact, heterogeneity and sustainability. Energy Economics. 2017; 62 ():187-194.
Chicago/Turabian StylePhilippe Barla; Mathieu Gilbert-Gonthier; Marco Antonio Lopez Castro; Luis Miranda-Moreno. 2017. "Eco-driving training and fuel consumption: Impact, heterogeneity and sustainability." Energy Economics 62, no. : 187-194.