- Email: [email protected]
Safety Analysis on Urban Arterials Considering Operational Conditions in Shanghai
Available online at www.sciencedirect.com
Procedia Engineering 45 (2012) 836 – 840
2012 International Symposium on Safety Science and Technology
Safety analysis on urban arterials considering operational conditions in Shanghai WANG Xuesong, CHEN Ming* Schoolf of Transportaiton Engineering, Road and Traffic Engineering Key Laboratory, Tongji University, Shanghai 201804, China
Abstract While it is generally agreed that traffic safety on urban arterials is closely associated with operational conditions, analysis of these relationships has been hampered by the absence of continuous measurements of operational variables such as traffic flow. Operational features of both peak and off peak were examined on a total 176 arterial segments from 23 different corridors within specific regions. These operational data, together with road segment characteristics, (e.g., segment length, number of lanes, median type), were used to construct models to estimate crash frequencies under various operational conditions for differing road segments. To account for the spatial correlations among the segments along the same corridors, Poisson-lognormal models with a two-level hierarchy under a Bayesian framework were used. Results showed significant relationships among operational conditions, roadway characteristics, and crash occurrence on these urban arterials. Lower average speeds at the corridor segment level were found to be associated with higher crash frequencies. The implications of using FCD data to assess operational conditions, and the use of hierarchical Bayesian models for predicting crash probabilities under different operational conditions are discussed.
© 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Beijing Institute of Technology. Keywords: urban arterial; road segment; safety analysis; operational condition; Floating Car Data; hierarchical models; Bayesian estimation
1. Introduction Most traffic on urban road networks is carried by arterials, yet these are the least safe of all roads in the urban network. Operational conditions on arterials constantly vary, and are therefore difficult to fully describe using traditional techniques, such as laser guns, and loop detectors that can only get speed information from fixed locations. Both the limited coverage and static nature of spot speed measurements, have hampered efforts to explore the dynamic relationships between continuously varying operational conditions and safety. This study uses a new method to address this problem. In Shanghai, more than 45,000 taxis equipped with Global Positional Systems (GPS) use the urban network during the daytime. This taxi GPS data, hereafter called Floating Car Data (FCD), provides updates of each taxi’s roadway location and position every 10 seconds. FCD has already been used to extract average speed or travel time in the road network. Actually, as an important part of the Intelligent Traffic System (ITS) in many cities in China, FCD technology has been widely applied to acquire information on traffic conditions. However, FCD also can be used to acquire continuous data on roadway operational conditions, as it allows for calculations of average speed, speed variance, and speed distributions along any roadways selected for study. FCD can provide the ability to explore relationships between crashes and the dynamic operational conditions on urban arterials. In this paper, unsignalized segments of 23 major arterials in the urban area of Shanghai were selected for study. The segment-level operational conditions, including average speed and speed variance
* Corresponding author. Tel.: +86-21-69583946; fax: +86-21-65989270. E-mail address: [email protected]
1877-7058 © 2012 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.08.247
837
WANG Xuesong and CHEN Ming / Procedia Engineering 45 (2012) 836 – 840
were acquired for both peak and off-peak periods. Poisson-lognormal Bayesian models were then developed to investigate the relationships among crash frequency, operational conditions, and other roadway characteristics. 2. Data preparation In this study, 23 arterials in urban areas of Shanghai were selected for study. These arterials are 67.7 km in total length. Resende and Benekohal[1] highlighted the importance of dividing the roadway into segments with homogenous characteristics. Following the guidance, the arterials were divided into several analysis segments, which were defined by the two signalized intersections that terminated them along the arterial. The selected corridors belong to two different urban areas of Shanghai, Puxi and Pudong. These are identified by the corridor-level dummy variable region. Roads in the Pudong area are generally newer than those in the Puxi area. Moreover, the length of the corridor, number of access points, access points per km, number of signalized intersections, and signalized intersections per km are also identified as corridor-level variables. The length of each segment and the number of signalized intersections were obtained using the GIS base map. The number of access points were manually determined using the software Google earth. The cross-section features, including number of lanes, median type, separator of motorized and bicycle lane, were collected by a field survey conducted along each corridor. Traffic volume during was acquired from loop detectors and average volume was computed for peak and off-peak period. Floating Car Data (FCD) provides vehicle ID, time, longitude and latitude, speed, and vehicle traveling direction with intervals of 10 to 20 seconds for each equipped vehicle. One can calculate the travel time between two GPS data transmissions according to the its traveled distance and time. The operational conditions on each segment were acquired during peak and off-peak periods from May 5th to 7th, 2009. Based on traffic volume data from loop detectors, the peak period occurred between 7:00-9:00 a.m. and the off-peak period occurred between was from 12:00-14:00 p.m. The mean speed of the segment during peak and off peak hours was then computed using the following formula:
1 where Vj is the travel speed of jth sample, and N is the number of vehicles comprising the samples. The mean speed indicates the average speed of vehicles sampled on the segment during the specific period. The speed variability was also calculated for each segment during peak and off peak hours on using the following formula:
2 All collisions including fatal and non-fatal collisions for the selected segments were collected for year 2009 from Shanghai traffic police reports. The geocoding procedure in ArcGIS software is used to locate the crashes on the GIS base map. Intersection related crashes were excluded when counting the crashes for each segment because these crashes were not related to variables associated with the operational condition of the segments. In order to explore the different relationship under different traffic conditions, peak (7:00-9:00 am) and off-peak (12-2:00pm) crashes were compared. 3. Modeling results of crash frequencies on urban arterials Two varying intercept models, for peak and off-peak periods respectively, were developed for urban arterials to investigate the relationship between crash frequencies and operational conditions as well as other roadway features. The single varying intercept and slope models with operational condition measures (variance and mean speed) only were developed to investigate the relationship between crash frequencies and average speed as well as variance among the different corridors. The road segments were assumed to be spatially correlated at the corridor-level, and segments from different corridors were assumed to be statistically independent. The models are constructed under Bayesian hierarchical framework. The posterior summaries were obtained via WinBUGS software using two chains with 25,000 iterations each, first 5,000 of which were excluded as a burn-in sample. The Deviance Information Criteria (DIC) corridor-level variations were calculated to compare the models.
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WANG Xuesong and CHEN Ming / Procedia Engineering 45 (2012) 836 – 840
Table 1 summarizes the parameters estimates and their 95% credible intervals for the peak period and off-peak period models, respectively. Fixed effects for both corridor-level variables and segment-level variables and random effects are estimated in the models. The DIC statistics were 770.339 and 726.36 under the peak period and off-peak period models, respectively. Table 1. Parameters Estimates for the Peak Period and Off-peak Period Models
Variables
Peak period Mean
Sd.
Off-peak period 95%CI
Mean
Sd.
95%CI
Fixed effects Corridor-level variables Intercept
0.3248
0.5709
(-0.745,1.307)
0.6298
0.385
(-0.084,1.142)*
Region 1.047
0.3204
(0.4185,1.697)
-
-
Number of signalized intersections
Puxi Area vs. Pudong Area
-0.0551
0.0263
(-0.108,-0.002)
-
-
-
Number of access points
0.07979
0.0265
(0.0319,0.138)
0.1021
0.059
(-0.014,0.221)*
Average speed
-0.0239
0.0099
(-0.043,-0.005)
-0.0184
0.011
(-0.04,-0.0006)
Segment length
0.002
0.0003
(0.0014,0.0027)
0.0016
0.0003
(0.0009,0.0022)
Total number of lanes
0.1289
0.0598
(0.007,0.241)
-
Segment-level variables
-
-
-
Median types Barrier vs. None
-
-
-
-0.3831
0.271
(-0.924,0.137)*
Median strip vs. None
-
-
-
0.1003
0.284
(-0.467,0.638)
Bicycle lane separator Barrier vs. No separator No bicycle lane vs. No separator
-0.0953
0.2825
(-0.6372,0.430)
-
-
-
-1.355
0.3728
(-2.089,-0.638)
-
-
-
Pedestrian crossing Yes vs. No
-
-
-
0.3495
0.211
(-0.066,0.764)*
-
-
-
0.3237
0.2017
(-0.06,0.7249)*
2 s
0.4426
0.0897
(0.2916,0.6434)
0.5026
0.1065
(0.324,0.7395)
2 0
0.1223
0.0956
(0.0058,0.3607)
0.4054
0.2057
(0.1309,0.9185)
Land use types Commercial vs. Not commercial Random effects
Corridor-level variation
21.64%
44.64%
DIC
770.339
726.36
Note: dash (-) means data not applicable; * means variable statistically significant from zero (90% credible sets show the same sign)
Among the variables investigated, region, number of signalized intersections, number of access points (peak period), length of segment, number of lanes, bicycle lane separator and average speed are identified as significant at 95% Credible Interval (CI). Other variables including number of access points, median type, pedestrian crossing and commercial land use in the off-period model are identified as significant at 90% credible interval. The estimates of are significant across peak period and off-peak period model, demonstrating that the effect of heterogeneity does exists in our data. The within-corridor correlation was 21.64% and 44.64% for peak period and off-peak period models, respectively. This indicated that the large between-corridor variability and necessity of the using hierarchical models.
WANG Xuesong and CHEN Ming / Procedia Engineering 45 (2012) 836 – 840
839
4. Variable interpretation and discussion Average speed and speed variance, which were obtained from FCD, were included in the models to describe the integrated operational conditions on segments. Average speed was found significantly associated with crash occurrence. The unexpected negative sign indicates that segments with lower speed are associated with higher crash frequencies. Obviously, this finding contradicts that of the prevalent and strongly held brief. The similar results could also been found in the previous studies[2,3]. Baruya[2] explained this negative relationship that factors examined do not stand alone, but interact with each other. And Garber and Gadiraju found that larger speed variances appeared to be associated with low average speeds at the examined roads. Considering the examined roads were urban arterials, the model result could be reasonable. The road network in Shanghai was generally at a low-speed condition. Arterials were extremely congested with huge amounts of traffic, especially during the peak period. Fig. 1 presents the speed distribution (travel time percentage) for different speed ranges for the selected segments. It shows a large percentage for the lower speed ranges (more than 35% for speed lower than 10km/h). In a word, the lower mean speed, generally associated with the interrupted traffic flow, implies higher crash occurrence on the segments.
Fig. 1. Speed profile along a typical corridor.
Region represents the two major areas in Shanghai, on which the road conditions are extremely different. As mentioned before, the road condition in Pudong Area is better as roads in Pudong Area newer. In the peak period model, the estimate for Region is 1.047, which indicates that crash frequencies in Puxi area are significantly higher than in the Pudong area. Number of signalized intersections along the corridor presents a negative relationship with the crash occurrence in the peak hour model, with the estimates of -0.0512. The explanation could be that corridors with more signalized intersections are associated with the more stable traffic flow, which resulted in lower crashes on the segments (excluding intersection) during the peak period. Number of Access points is identified as a corridor-level variable and represents the total number of access points along the corridor through in both directions. The result shows that number of access points has significant impacts on crash frequencies on urban arterials. In general, the increase of access points will increase the crash occurrence in both peak period and off-peak period. Length of segment is the most significant variable to crash occurrence on urban arterials. The coefficients of segment length are 0.002 and 0.0016 for the peak hour and off-peak hour models respectively, which indicate the increase of the segment length, will significantly increase the crash frequencies on urban arterials. This conclusion has been proven in many studies[4,5]. Number of lanes is identified as the total number of lanes of segment varying from 4 to 10 in the investigated data. In most previous studies[4-6], this variable has been proved to be a significant variable to the crash frequencies on urban roads. Although it is associated with the other road design variables, more lanes/wider lanes would increase of exposure of crash. For example, drivers have more opportunities to change lane on the segments with more lanes. This do exists on the urban roads, especially when the segments are congested; drivers always tend to change lanes often in order to move forward. Hence, in the peak period model, the coefficients of number of lanes indicates that increase of number of lanes is associated with the increase crash occurrence.
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WANG Xuesong and CHEN Ming / Procedia Engineering 45 (2012) 836 – 840
Median can effectively reduce the conflicts from opposite traffic, while separator of motorized and bicycle lane can reduce the conflicts between vehicles and cyclists. Although the median strip was not found significant variables to crash occurrence, the coefficient of median barrier indicates that segments with median barrier are associated with lower crash frequencies. In Shanghai, the road segments are mixed with huge amounts of bicycles and motor vehicles in the peak period. Hence, separator of motorized and bicycle lane is an important factor to affect the crash frequencies. It is statistically significant that segments with no bicycle lane have lower crash frequencies than those with no separator. However, there is no evidence from the result that the barrier separator can effectively reduce the crash occurrence. Other variables as pedestrian crossing and commercial land use are also found significant in the off-peak period model. The coefficient of Pedestrian crossing indicates that the segments with pedestrians crossing have higher crash frequencies. Similarly, the commercial land use is always associated with more pedestrian movements. Hence, crash frequencies on the commercial land use segments are higher than non-commercial land use segments. 5. Conclusions This study introduced the use of Floating Car Data to describe the operational conditions and investigated the relationship between crash occurrence and operational conditions in addition to other road characteristics on urban arterials. Instead of the spot speed acquired by traditional data collecting technique, FCD provide more potential information to describe the integrated and complex operational conditions on urban arterials as both individual-level speed and segmentlevel operational conditions can be extracted. The mean speed was used to represent the general speed performance on the arterial segment. The speed variance was introduced as the deviation from average speed, which can describe the fluctuant speed range for the observed samples. The mean speed and speed variance of the selected segments were extracted for both peak period and off-peak period. In addition to the operational conditions, road alignment, cross-section, traffic flow and other roadside conditions were collected along the urban arterial. Considering the spatial correlation from corridors, Poisson-lognormal models with twolevel hierarchy were established to estimate the effect of operational conditions and road characteristics to crash occurrence. The analyses were conducted under Bayesian framework by WinBUGS software, which provides the flexibility to model random intercept and random slope effects. The significant road characteristics were region, number of signalized intersections, number of access points, length of segment, median type, separator of motorized and bicycle lane, pedestrian crossing and commercial land use. The mean speeds at segment-level were found to influence the crash occurrence significantly and the negative relationship indicated that decrease of speed will increase the crash frequencies. This can be explained that lower speed is mostly resulted by the interrupted traffic flow on urban roads, which conceals more potential conflicts. The relationship between variance and crash occurrence is insignificant on the arterial segments, while the variance were found correlated to the mean speed strongly.
Acknowledgements Supported by Chinese National Science Foundation (51008230) and Road and Traffic Engineering Key Laboratory of Tongji University, Ministry of Education.
References [1] Resende, P., and Benekohal, R, 1997. Effect of Roadway Section Length on Accident Modeling. Traffic Congestion and Traffic Safety In The 21 Century Conference, ASCE, Chicago, IL. [2] Garber, N.J., Gadiraju, R., 1989. Factors affecting speed variance and its influence on accidents. Transportation Research Record No.1213, 64-71. [3] Baruya, B. 1998. Speed-accident relationships on European roads. In:Proceedings of the conference ‘Road safety in Europe’, Bergisch Gladbach, Germany, September 21–23, 1998, VTI Konferens No.10A, Part 10, pp. 1–17 [4] EI-Basyouny, K., Sayed, T, 2009. Accident prediction models with random corridor parameters. Accident Analysis and Prevention, 41(5), 1118–1123. [5] Abdel-Aty, M. and Radwan, E.A. 1999. Modeling traffic accident occurrence and involvement. Accident Analysis and Prevention, 32(5) , 633–642. [6] Greibe, P, 2003. Accident prediction models for urban roads. Accident Analysis & Prevention 35(2), 273-285.
Procedia Engineering 45 (2012) 836 – 840
2012 International Symposium on Safety Science and Technology
Safety analysis on urban arterials considering operational conditions in Shanghai WANG Xuesong, CHEN Ming* Schoolf of Transportaiton Engineering, Road and Traffic Engineering Key Laboratory, Tongji University, Shanghai 201804, China
Abstract While it is generally agreed that traffic safety on urban arterials is closely associated with operational conditions, analysis of these relationships has been hampered by the absence of continuous measurements of operational variables such as traffic flow. Operational features of both peak and off peak were examined on a total 176 arterial segments from 23 different corridors within specific regions. These operational data, together with road segment characteristics, (e.g., segment length, number of lanes, median type), were used to construct models to estimate crash frequencies under various operational conditions for differing road segments. To account for the spatial correlations among the segments along the same corridors, Poisson-lognormal models with a two-level hierarchy under a Bayesian framework were used. Results showed significant relationships among operational conditions, roadway characteristics, and crash occurrence on these urban arterials. Lower average speeds at the corridor segment level were found to be associated with higher crash frequencies. The implications of using FCD data to assess operational conditions, and the use of hierarchical Bayesian models for predicting crash probabilities under different operational conditions are discussed.
© 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Beijing Institute of Technology. Keywords: urban arterial; road segment; safety analysis; operational condition; Floating Car Data; hierarchical models; Bayesian estimation
1. Introduction Most traffic on urban road networks is carried by arterials, yet these are the least safe of all roads in the urban network. Operational conditions on arterials constantly vary, and are therefore difficult to fully describe using traditional techniques, such as laser guns, and loop detectors that can only get speed information from fixed locations. Both the limited coverage and static nature of spot speed measurements, have hampered efforts to explore the dynamic relationships between continuously varying operational conditions and safety. This study uses a new method to address this problem. In Shanghai, more than 45,000 taxis equipped with Global Positional Systems (GPS) use the urban network during the daytime. This taxi GPS data, hereafter called Floating Car Data (FCD), provides updates of each taxi’s roadway location and position every 10 seconds. FCD has already been used to extract average speed or travel time in the road network. Actually, as an important part of the Intelligent Traffic System (ITS) in many cities in China, FCD technology has been widely applied to acquire information on traffic conditions. However, FCD also can be used to acquire continuous data on roadway operational conditions, as it allows for calculations of average speed, speed variance, and speed distributions along any roadways selected for study. FCD can provide the ability to explore relationships between crashes and the dynamic operational conditions on urban arterials. In this paper, unsignalized segments of 23 major arterials in the urban area of Shanghai were selected for study. The segment-level operational conditions, including average speed and speed variance
* Corresponding author. Tel.: +86-21-69583946; fax: +86-21-65989270. E-mail address: [email protected]
1877-7058 © 2012 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.08.247
837
WANG Xuesong and CHEN Ming / Procedia Engineering 45 (2012) 836 – 840
were acquired for both peak and off-peak periods. Poisson-lognormal Bayesian models were then developed to investigate the relationships among crash frequency, operational conditions, and other roadway characteristics. 2. Data preparation In this study, 23 arterials in urban areas of Shanghai were selected for study. These arterials are 67.7 km in total length. Resende and Benekohal[1] highlighted the importance of dividing the roadway into segments with homogenous characteristics. Following the guidance, the arterials were divided into several analysis segments, which were defined by the two signalized intersections that terminated them along the arterial. The selected corridors belong to two different urban areas of Shanghai, Puxi and Pudong. These are identified by the corridor-level dummy variable region. Roads in the Pudong area are generally newer than those in the Puxi area. Moreover, the length of the corridor, number of access points, access points per km, number of signalized intersections, and signalized intersections per km are also identified as corridor-level variables. The length of each segment and the number of signalized intersections were obtained using the GIS base map. The number of access points were manually determined using the software Google earth. The cross-section features, including number of lanes, median type, separator of motorized and bicycle lane, were collected by a field survey conducted along each corridor. Traffic volume during was acquired from loop detectors and average volume was computed for peak and off-peak period. Floating Car Data (FCD) provides vehicle ID, time, longitude and latitude, speed, and vehicle traveling direction with intervals of 10 to 20 seconds for each equipped vehicle. One can calculate the travel time between two GPS data transmissions according to the its traveled distance and time. The operational conditions on each segment were acquired during peak and off-peak periods from May 5th to 7th, 2009. Based on traffic volume data from loop detectors, the peak period occurred between 7:00-9:00 a.m. and the off-peak period occurred between was from 12:00-14:00 p.m. The mean speed of the segment during peak and off peak hours was then computed using the following formula:
1 where Vj is the travel speed of jth sample, and N is the number of vehicles comprising the samples. The mean speed indicates the average speed of vehicles sampled on the segment during the specific period. The speed variability was also calculated for each segment during peak and off peak hours on using the following formula:
2 All collisions including fatal and non-fatal collisions for the selected segments were collected for year 2009 from Shanghai traffic police reports. The geocoding procedure in ArcGIS software is used to locate the crashes on the GIS base map. Intersection related crashes were excluded when counting the crashes for each segment because these crashes were not related to variables associated with the operational condition of the segments. In order to explore the different relationship under different traffic conditions, peak (7:00-9:00 am) and off-peak (12-2:00pm) crashes were compared. 3. Modeling results of crash frequencies on urban arterials Two varying intercept models, for peak and off-peak periods respectively, were developed for urban arterials to investigate the relationship between crash frequencies and operational conditions as well as other roadway features. The single varying intercept and slope models with operational condition measures (variance and mean speed) only were developed to investigate the relationship between crash frequencies and average speed as well as variance among the different corridors. The road segments were assumed to be spatially correlated at the corridor-level, and segments from different corridors were assumed to be statistically independent. The models are constructed under Bayesian hierarchical framework. The posterior summaries were obtained via WinBUGS software using two chains with 25,000 iterations each, first 5,000 of which were excluded as a burn-in sample. The Deviance Information Criteria (DIC) corridor-level variations were calculated to compare the models.
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WANG Xuesong and CHEN Ming / Procedia Engineering 45 (2012) 836 – 840
Table 1 summarizes the parameters estimates and their 95% credible intervals for the peak period and off-peak period models, respectively. Fixed effects for both corridor-level variables and segment-level variables and random effects are estimated in the models. The DIC statistics were 770.339 and 726.36 under the peak period and off-peak period models, respectively. Table 1. Parameters Estimates for the Peak Period and Off-peak Period Models
Variables
Peak period Mean
Sd.
Off-peak period 95%CI
Mean
Sd.
95%CI
Fixed effects Corridor-level variables Intercept
0.3248
0.5709
(-0.745,1.307)
0.6298
0.385
(-0.084,1.142)*
Region 1.047
0.3204
(0.4185,1.697)
-
-
Number of signalized intersections
Puxi Area vs. Pudong Area
-0.0551
0.0263
(-0.108,-0.002)
-
-
-
Number of access points
0.07979
0.0265
(0.0319,0.138)
0.1021
0.059
(-0.014,0.221)*
Average speed
-0.0239
0.0099
(-0.043,-0.005)
-0.0184
0.011
(-0.04,-0.0006)
Segment length
0.002
0.0003
(0.0014,0.0027)
0.0016
0.0003
(0.0009,0.0022)
Total number of lanes
0.1289
0.0598
(0.007,0.241)
-
Segment-level variables
-
-
-
Median types Barrier vs. None
-
-
-
-0.3831
0.271
(-0.924,0.137)*
Median strip vs. None
-
-
-
0.1003
0.284
(-0.467,0.638)
Bicycle lane separator Barrier vs. No separator No bicycle lane vs. No separator
-0.0953
0.2825
(-0.6372,0.430)
-
-
-
-1.355
0.3728
(-2.089,-0.638)
-
-
-
Pedestrian crossing Yes vs. No
-
-
-
0.3495
0.211
(-0.066,0.764)*
-
-
-
0.3237
0.2017
(-0.06,0.7249)*
2 s
0.4426
0.0897
(0.2916,0.6434)
0.5026
0.1065
(0.324,0.7395)
2 0
0.1223
0.0956
(0.0058,0.3607)
0.4054
0.2057
(0.1309,0.9185)
Land use types Commercial vs. Not commercial Random effects
Corridor-level variation
21.64%
44.64%
DIC
770.339
726.36
Note: dash (-) means data not applicable; * means variable statistically significant from zero (90% credible sets show the same sign)
Among the variables investigated, region, number of signalized intersections, number of access points (peak period), length of segment, number of lanes, bicycle lane separator and average speed are identified as significant at 95% Credible Interval (CI). Other variables including number of access points, median type, pedestrian crossing and commercial land use in the off-period model are identified as significant at 90% credible interval. The estimates of are significant across peak period and off-peak period model, demonstrating that the effect of heterogeneity does exists in our data. The within-corridor correlation was 21.64% and 44.64% for peak period and off-peak period models, respectively. This indicated that the large between-corridor variability and necessity of the using hierarchical models.
WANG Xuesong and CHEN Ming / Procedia Engineering 45 (2012) 836 – 840
839
4. Variable interpretation and discussion Average speed and speed variance, which were obtained from FCD, were included in the models to describe the integrated operational conditions on segments. Average speed was found significantly associated with crash occurrence. The unexpected negative sign indicates that segments with lower speed are associated with higher crash frequencies. Obviously, this finding contradicts that of the prevalent and strongly held brief. The similar results could also been found in the previous studies[2,3]. Baruya[2] explained this negative relationship that factors examined do not stand alone, but interact with each other. And Garber and Gadiraju found that larger speed variances appeared to be associated with low average speeds at the examined roads. Considering the examined roads were urban arterials, the model result could be reasonable. The road network in Shanghai was generally at a low-speed condition. Arterials were extremely congested with huge amounts of traffic, especially during the peak period. Fig. 1 presents the speed distribution (travel time percentage) for different speed ranges for the selected segments. It shows a large percentage for the lower speed ranges (more than 35% for speed lower than 10km/h). In a word, the lower mean speed, generally associated with the interrupted traffic flow, implies higher crash occurrence on the segments.
Fig. 1. Speed profile along a typical corridor.
Region represents the two major areas in Shanghai, on which the road conditions are extremely different. As mentioned before, the road condition in Pudong Area is better as roads in Pudong Area newer. In the peak period model, the estimate for Region is 1.047, which indicates that crash frequencies in Puxi area are significantly higher than in the Pudong area. Number of signalized intersections along the corridor presents a negative relationship with the crash occurrence in the peak hour model, with the estimates of -0.0512. The explanation could be that corridors with more signalized intersections are associated with the more stable traffic flow, which resulted in lower crashes on the segments (excluding intersection) during the peak period. Number of Access points is identified as a corridor-level variable and represents the total number of access points along the corridor through in both directions. The result shows that number of access points has significant impacts on crash frequencies on urban arterials. In general, the increase of access points will increase the crash occurrence in both peak period and off-peak period. Length of segment is the most significant variable to crash occurrence on urban arterials. The coefficients of segment length are 0.002 and 0.0016 for the peak hour and off-peak hour models respectively, which indicate the increase of the segment length, will significantly increase the crash frequencies on urban arterials. This conclusion has been proven in many studies[4,5]. Number of lanes is identified as the total number of lanes of segment varying from 4 to 10 in the investigated data. In most previous studies[4-6], this variable has been proved to be a significant variable to the crash frequencies on urban roads. Although it is associated with the other road design variables, more lanes/wider lanes would increase of exposure of crash. For example, drivers have more opportunities to change lane on the segments with more lanes. This do exists on the urban roads, especially when the segments are congested; drivers always tend to change lanes often in order to move forward. Hence, in the peak period model, the coefficients of number of lanes indicates that increase of number of lanes is associated with the increase crash occurrence.
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WANG Xuesong and CHEN Ming / Procedia Engineering 45 (2012) 836 – 840
Median can effectively reduce the conflicts from opposite traffic, while separator of motorized and bicycle lane can reduce the conflicts between vehicles and cyclists. Although the median strip was not found significant variables to crash occurrence, the coefficient of median barrier indicates that segments with median barrier are associated with lower crash frequencies. In Shanghai, the road segments are mixed with huge amounts of bicycles and motor vehicles in the peak period. Hence, separator of motorized and bicycle lane is an important factor to affect the crash frequencies. It is statistically significant that segments with no bicycle lane have lower crash frequencies than those with no separator. However, there is no evidence from the result that the barrier separator can effectively reduce the crash occurrence. Other variables as pedestrian crossing and commercial land use are also found significant in the off-peak period model. The coefficient of Pedestrian crossing indicates that the segments with pedestrians crossing have higher crash frequencies. Similarly, the commercial land use is always associated with more pedestrian movements. Hence, crash frequencies on the commercial land use segments are higher than non-commercial land use segments. 5. Conclusions This study introduced the use of Floating Car Data to describe the operational conditions and investigated the relationship between crash occurrence and operational conditions in addition to other road characteristics on urban arterials. Instead of the spot speed acquired by traditional data collecting technique, FCD provide more potential information to describe the integrated and complex operational conditions on urban arterials as both individual-level speed and segmentlevel operational conditions can be extracted. The mean speed was used to represent the general speed performance on the arterial segment. The speed variance was introduced as the deviation from average speed, which can describe the fluctuant speed range for the observed samples. The mean speed and speed variance of the selected segments were extracted for both peak period and off-peak period. In addition to the operational conditions, road alignment, cross-section, traffic flow and other roadside conditions were collected along the urban arterial. Considering the spatial correlation from corridors, Poisson-lognormal models with twolevel hierarchy were established to estimate the effect of operational conditions and road characteristics to crash occurrence. The analyses were conducted under Bayesian framework by WinBUGS software, which provides the flexibility to model random intercept and random slope effects. The significant road characteristics were region, number of signalized intersections, number of access points, length of segment, median type, separator of motorized and bicycle lane, pedestrian crossing and commercial land use. The mean speeds at segment-level were found to influence the crash occurrence significantly and the negative relationship indicated that decrease of speed will increase the crash frequencies. This can be explained that lower speed is mostly resulted by the interrupted traffic flow on urban roads, which conceals more potential conflicts. The relationship between variance and crash occurrence is insignificant on the arterial segments, while the variance were found correlated to the mean speed strongly.
Acknowledgements Supported by Chinese National Science Foundation (51008230) and Road and Traffic Engineering Key Laboratory of Tongji University, Ministry of Education.
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