2.2. luís gonçalo campos, paulo fernandes, margarida c. coelho
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S4C Colloquium Aveiro 2016 https://scientistsforcyclingaveiro2016.wordpress.com/ University of Aveiro (Portugal), Region of Aveiro (CIRA), ABIMOTA/Portugal Bike Value and the European Cyclists’ Federation (ECF) with its global network Scientists for Cycling (S4C)
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2.2. luís gonçalo campos, paulo fernandes, margarida c. coelho
- 1. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 1 Safer cycling routes to the university: Analysis of conflicts between motor vehicles and bicycles Luís Gonçalo Campos, Paulo Fernandes, Margarida C. Coelho University of Aveiro, R&D Group on Transportation Technology, Centre for Mechanical Technology and Automation (TEMA) / Department of Mechanical Engineering
- 2. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 2 Introduction Cyclists in cities? Portugal recorded 7,697 accidents involving bicycles in 2010-2014 period, which resulted in 307 fatalities (ANSR, 2016) The increasing number of bicycles on the road infrastructure causes more conflicts with motor vehicles; An argument concerning the use of bicycles in cities deals with safety and concerns with the presence and unexpected behaviors of motorized users.
- 3. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 3 Objectives How do the number of conflicts and the probability of collisions between vehicles and bicycles vary with different bicycle facilities, and variations in bicycle demands and speeds? How do the design features of a specific bicycle route affect the spatial distribution of road safety indicators?
- 4. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 4 Methodology – Case Study Source: https://maps.google.com/ Route A 2.4 km Route B 2.3 km Route C 3.95 km Origin: Aveiro Train Station Destination: University of Aveiro (Rectory) Origin Destination Origin Destination Origin Destination Dedicate bicycle path Route A Dedicate bicycle path Route B Dedicate bicycle path Route C
- 5. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 5 Methodology – Data Collection • Traffic and Bicycle volumes; • Vehicle GPS traces; • Origin-Destination Matrices; • Road and Intersection geometry. Dedicate bicycle path Bicycle counts point Source: https://maps.google.com/ Origin Destination Morning peak conditions (7:30-9:00 a.m.) 780 vehicles 13 cyclists 690 vehicles 17 cyclists 630 vehicles 25 cyclists 490 vehicles 15 cyclists 650 vehicles 25 cyclists 340 vehicles 18 cyclists
- 6. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 6 Methodology – Traffic and Safety Modeling • Time to Collision (TTC) • Post-Encroachment Time (PET); • Initial Deceleration Rate (DR); • Maximum speed (MaxS); • Maximum relative speed difference (DeltaS); • Types and location of the conflicts. SSAM Model Vehicles Trajectories (Files *.trj) • Car Fleet; • Bicycle Volumes; • Routes Definition; • Driving behavior patterns; • Speed and Acceleration; Inputs (VISSIM) • Delay • Vehicle Stops Traffic Outputs (VISSIM) Safety Outputs (SSAM)
- 7. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 7 Methodology – Safety Modeling If during an interaction TTC and PET drop below 1.5 s and 5.0 s Types of Conflicts if 0º < conflict angle < 30° if 30º < conflict angle < 85° if 85º < conflict angle < 180° Rear end Crossing Lane Changing Conflict
- 8. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 8 Methodology – Alternative Scenarios Baseline Network-specific traffic and bicycling demands Scenario 1 (S1) Dedicated bicycle lanes along the entire routes and = number of bicycles; Scenario 2 (S2) Increase the number of bicycles in 10x and = number of bicycles; Scenario 3 (S3) Speed limit of 30 km/h on the roads alongside of routes and = number of bicycles.
- 9. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 9 Results – Traffic Performance 70 72 74 76 78 80 82 84 86 88 90 0 5,000 10,000 15,000 20,000 25,000 Baseline Scenario S1 S2 S3 Delay[Seconds] Numberofstops Number of stops Number of Vehicles Delay
- 10. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 10 Results – Number of Conflicts Route Scenario Crossing Rear End Lane Change Total A Baseline 7.7 244.1 61.1 312.9 S1 -3% -29% -23% -27% S2 -4% 17% 20% 17% S3 0% 1% -3% 0% B Baseline 7.7 244.1 61.1 312.9 S1 -10% -29% -24% -28% S2 -6% 20% 22% 20% S3 1% 4% -4% 2% C Baseline 7.7 244.1 61.1 312.9 S1 29% -5% -3% -4% S2 243% 12% 39% 19% S3 143% 28% 22% 27% Lowest value among scenariosHighest value among scenarios
- 11. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 11 Results – Hotspot Conflicts Location Route A Route B Route C
- 12. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 12 Conclusions S2 and S3 resulted in more delay and vehicles stops while S1 improved traffic performance compared to the baseline; There was not a consensus about the safest route; S1 reduced the number and the severity conflicts for all routes, but S3 was associated with the lowest severity of resulting crashes; Hotspot conflicts locations were located near multi- lane roundabouts and signalized intersections.
- 13. Scientists for Cycling Colloquium November 17-19, 2016 21/11/2016 13 Transportation Technology Research Group - Centre for Mechanical Technology and Automation (TEMA), University of Aveiro, paulo.fernandes@ua.pt Thank you for your attention.
Editor's Notes
- My name is Paulo Fernandes, I’m a PhD student in the Mechanical Engineering Department in The University of Aveiro. Next, I will present the following presentation entitled Safer cycling routes to the university: Analysis of conflicts between motor vehicles and bicycles That offers a research line of cyclists safety by using a microsimulation platform of traffic and safety in a real-world case study. This work was master thesis that was conducted by Luis Gonçalo Campos
- Between 2010 and 2014, almost 8,000 accidents involving cyclists were recorded in Portugal. From these, 307 were killed and almost 1,000 resulted in serious injurious. Due to the increasing of bike users in cities routes, more conflicts with motor-vehicles can be observed. One of the most relevant arguments given by people for not regularly use the bike is the lack of safety and some concerns with the presence and unexpected behaviours of motorized users.
- With these concerns in mind, the main objectives of this work is two-fold: To address the variation in the number of conflicts and crashes' probability along urban roads with different bicycle facilities and demands, and posted speed limits; To assess the impact of a specific bicycle route on the spatial distribution of conflicts.
- An urban environment in the city of Aveiro, Portugal, exhibiting high road traffic volumes, was sought out for this research. Aveiro in one of the five municipalities with the highest absolute values of cyclists in Portugal. This analysis was focused on three alternative routes that connect an origin point (city train station) to the arrival point (UA). More than 10,000 people who come daily by train from other cities to Aveiro travel through this O-D pair by car, walking or cycling. Routes A and B are approximately 2.3 km long, while Route C is longer, almost 4.0 km in length. While the first two routes are preferably chosen by cyclists, Route C is the least used (because of the extra time required), but has the interest of having a dedicated cycle lane and low congestion levels.
- To assess the baseline scenario, several network variables were collected. The monitoring plan proceeded in three parts. First, 10 different routes with heterogeneous traffic conditions across the study domain were covered using GPS equipped vehicles to collect second-by-second vehicle dynamics (speed, acceleration-deceleration and road grade). Second, traffic volumes were monitored in 56 strategic points that allow the connection among relevant roads within the study network. Based on the above data, time dependent O-D matrices were defined for each intersection and assigned to the overall study domain. Step three was focused on quantifying bicycle volumes at 6 locations. These data were collected during the morning peak period (7.30-9:00 a.m.) in typical weekdays of April 2015 (Tuesdays, Wednesdays and Thursdays), and under dry weather conditions. The percentage of bicycles by motor vehicles in the data collection points goes from 1.8% to 4.8%. Bicycle O-D matrices were also defined for each route, and further used to calibrate VISSIM traffic model.
- The widely used microscopic simulation package VISSIM was used in this work to simulate traffic operations. The major benefit of VISSIM is that a high level of details can be reached in modeling the characteristics of road network, vehicle and bicycle performance measures such as the speed and the desired maximum braking and acceleration values, and driver’s behaviors. VISSIM computes several traffic performance measures such as delay and number of stop-and-go cycles that were used to assess the study domain. Also, it is able to export the trajectories of vehicles that can be used by external models to assess the safety. In this research, the Surrogate Safety Assessment Model (SSAM) was used to estimate the traffic conflicts between vehicles and cyclists. SSAM was developed by The Federal Highway Administration (FHWA) in the United States. From these conflicts, SSAM computes the following measures: time-to-collision (TTC), post-encroachment time (PET) and deceleration rate (DR), the relative difference in vehicles and cyclist speed (DeltaS) and the maximum speed (MaxS). The first two measures evaluates the severity of conflict (lower values indicate severe conflicts) while other measures were used as proxies for the crash severity.
- But how SSAM computes conflicts? Basically, SSAM evaluates all interactions and If at any time the TTC and PET drop below a given threshold [1.5 s and 5.0 s, respectively, as suggested by scientific literature), the interaction is tagged as a conflict. Then DR, DeltaS and MaxS are calculated at the instant of the minimum TTC. SSAM classifies resulting conflicts into three categories based on a conflict angle (from -180° to +180°): rear end if 0º < conflict angle <30°, a crossing conflict if 85º < conflict angle < 180°, otherwise is a lane change conflict.
- Baseline scenario corresponds to the existing site-specific operations during the morning peak period in which traffic and bicycles were assigned to their corresponding routes. To assess traffic performance and road safety indicators, three alternative scenarios were established: Scenario 1 (S1): creation and implementation of dedicated bicycle lanes along the entire routes A, B and C, while maintaining the same bicycle users; Scenario 2 (S2): increase the number of bicycles in 10 times, but keeping the number of motor vehicles from the baseline scenario; Scenario 3 (S3): adoption of speed limit of 30 km/h on the roads alongside of routes A, B and C, and assuming the same number of bicycles as the baseline scenario.
- This slide compares the number of vehicles, stop and-go situations, and the vehicle delay by scenario. Main observations: The lowest number of stop-and-go situations was observed in the scenario S1. This happens because all routes have dedicated lanes throughout their length, thus vehicles do not necessarily stop when they approach or overtake cyclists. S2 is associated with poor performance levels among scenarios. It has an increase of 3% and 4% in delay and stop-and-go situations, respectively, in comparison with baseline scenario; The creation of low speed zones (S3) induces an increase of stop-and-go situations and delay, respectively; in addition, vehicles spend less time in free-flow conditions when compared with the baseline scenario.
- Concerning the safety, this slide compares the number of vehicle-bicycle conflicts and surrogate safety measures by scenario. For Route A, S1 is the best safety solution both for rear-end and lane change conflicts. In spite of having less crossing conflicts, S2 lead to an increase in the number of conflicts compared to the baseline scenarios. This is explained by the number of bicycles in the network, which increases the likelihood of conflicts. A similar trend is observed in Route B. For instance, S3 does not represent a safety benefit. It leads to an increase in crossing and rear-end conflicts while lane-change conflicts are fairly similar than those obtained in the baseline scenario; This alternative scenario is the worst safety solution in Route C. The number of conflicts increases in S2 while S1 has the lowest number of rear-end and lane-change conflicts. Despite these results, there were not a consensus in terms of road safety measures. For instance, S3 resulted in less severe potential crashes, and S1 and S2 in less severe conflicts. However, the differences were not stastically significant in relation to the baseline.
- Here, the hotspot vehicle-bicycle conflicts locations of each route under the conditions of the baseline scenario are presented. The analysis performed in SSAM demonstrates that the influence areas of roundabouts (namely the two-lane layout) and signalized intersections along the routes are sensitive spots in terms of road conflicts (overall contribution on total vehicle-bicycle conflicts is approximately 70%). Analyzes of alternative scenarios resulted in the same conclusions as the baseline scenario.
- In summary, the main conclusions of this work were: S2 recorded the highest delay and vehicle stops while S1 improved traffic performance compared to the existing situation; There was not a consensus about the most safety route; Some alternative scenarios lead to fewer vehicle-bicycle conflicts, but these were more severe than those obtained in the baseline scenario; The findings also confirmed that approximately 70% of the vehicle-bicycle conflicts were located near multi-lane roundabouts and signalized intersections.
- In summary, the main conclusions of this work were: S2 recorded the highest delay and vehicle stops while S1 improved traffic performance compared to the existing situation; There was not a consensus about the most safety route; Some alternative scenarios lead to fewer vehicle-bicycle conflicts, but these were more severe than those obtained in the baseline scenario; The findings also confirmed that approximately 70% of the vehicle-bicycle conflicts were located near multi-lane roundabouts and signalized intersections.