ZHANG, BIGHAM, LI, RAGLAND, and CHEN 1

Transcription

1 ZHANG, BIGHAM, LI, RAGLAND, and CHEN Associations between Road Network Connectivity and Pedestrian-Bicyclist Accidents Yuanyuan ZHANG PhD Student, School of Transportation Engineering, Tongji University Visiting Scholar, Safe Transportation Research & Education Center Institute of Transportation Studies, UC Berkeley Dwight Way, Mail code # Berkeley, CA 0- Tel: 0 yuanyuanzhang@berkeley.edu; chanel0@hotmail.com John BIGHAM GIS Program Manager Safe Transportation Research & Education Center Institute of Transportation Studies, UC Berkeley Dwight Way, Mail code # Berkeley, CA 0- jbigham@berkeley.edu Zhibin LI PhD student, Southeast University, P. R. of China Visiting Scholar in Safe Transportation Research & Education Center Institute of Transportation Studies, UC Berkeley Dwight Way, Mail code # Berkeley, CA 0- lizhibin@berkeley.edu David RAGLAND Director, Safe Transportation Research & Education Center Institute of Transportation Studies, UC Berkeley Dwight Way, Mail code # Berkeley, CA 0- Tel: davidr@berkeley.edu Xiaohong CHEN (corresponding author) Professor, School of Transportation Engineering, Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University 00 Cao an Road, Shanghai, 00, P. R. of China Tel) +--0 Fax) chenxh@tongji.edu.cn Word Count: (Text) + 00( Figures) + 000( Tables) = words Submission date: Nov., 0

2 ZHANG, BIGHAM, LI, RAGLAND, and CHEN 0 0 Abstract It has been extensively accepted that the road network connectivity can positively impact the propensity and duration of non-motorized travel. But its impact on non-motorist traffic safety is still under debate: on one side, well-connected road network could lead more through traffic into the core area of a region so that pedestrians and bicyclists would be more frequently exposed to conflicts with cars; on the other side, it could be safer when vehicle speed is slowed down by dense intersections and drivers are forced to concentrate on surroundings by active walking and bicycling. This debate stimulates the paper to estimate the associations between road network connectivity and pedestrian-bicyclist crashes. Four commonly utilized connectivity measures including block density, intersection density, street density, and mean block length are calculated based on the road networks of census tracts in Alameda County, California. Then the four measures together with other factors like traffic behavior, land use, transportation facility, and demographic feature are employed separately in a spatial statistical model called geographically weighted regression. Conclusions are: first, the decrease of pedestrian-bicyclist accident is significantly related to higher block density, higher intersection density, higher street density, and shorter mean block length; second, compared with other three connectivity measures, street density is better for modeling because of its higher stability and stronger explanatory ability; third, employing street network, traffic behavior, and transportation facility data into the same model can produce the best model fitness. Keywords Road network, connectivity, pedestrians, bicyclists, accidents

3 ZHANG, BIGHAM, LI, RAGLAND, and CHEN INTRODUCTION The function of a road network is to connect spatially separated places and to satisfy movement from origins to destinations. How well a network is connected---the connectivity influences the accessibility to potential destinations and it has important implications for travel mode choices, traffic performance and level of service especially for pedestrians and bicyclists (). In addition, connectivity is more clearly defined, easily calculated, and intuitive rather than other network pattern characteristics like centrality or circuitness. So a growing number of U.S. regional governments adopt connectivity measures as criteria for street planning, design and management.. Background Increased network connectivity is thought to be able to reduce travel distances and time which are particularly important for walking and bicycling because of their slower speeds (). Thus, many studies have investigated the impacts of connectivity on the propensity and duration of non-automobile trips. Increased road network connectivity is associated with more walking and biking. However, the existence of an association between connectivity and duration or trip length of walking and biking is not so significant (; ). An additional benefit of increased connectivity is having a wider range of routes to choose from for both pedestrians and bicyclists (). The effects of connectivity on pedestrians and bicyclists are extensively examined and proved. Thus, the traffic safety of these road users should also be associated with road connectivity, but information on this topic is limited. Recognized as the most vulnerable road users, pedestrians and bicyclists are frequently the focus of traffic safety research. Multitudes of factors have been included in analyses, including vehicle characteristics, roadway design characteristics, road user behaviors, and environmental conditions. The frequency of non-motorist crashes is found to be impacted by the age and gender of the individuals, proximity to elementary school, number of lanes and speed limit (). The injury severity of non-motorists has been shown to be related to the age of the individual, the speed limit on the roadway, location, and time-of-day (). For the severity of bicyclist involved crashes, other contributing factors are vehicle speed, age, weather, light (), and living location (). Other studies have been conducted focusing on pedestrian accidents and they evaluate the impacts of factors including vehicle type, driver and pedestrian features, time, light condition, economic feature, and road classification (; ). Recent work has begun to investigate the effect of street pattern and compactness on the severity of crashes involving vulnerable road users (0). Connectivity as a basic road network feature should be involved in safety researches for pedestrians and bicyclists. However, very limited research focuses on this topic. Although very few studies have analyzed the connectivity together with pedestrian-bicyclist crashes, several research have been conducted to investigate the relationship between connectivity and traffic safety for all road users. However, this relationship is still under debate. The first study to compare accident rates between grid pattern and curvilinear pattern in mid 0s showed that the grid pattern had substantially higher accident rate than limited-access pattern. Although this study may have several limitations including control of variables, series of recent studies, using statistical models, still imply that discontinuous networks like loops and lollipops perform safer than grid iron pattern (). On the other side, research about traffic safety performances of different types of urban places indicate increased street network connectivity is associated with increased safety. The more compact the street is, the safer the traffic movement of the area could be (). A Road diet is assessed to be able to reduce both crash frequency and crash rate (). The highest risk of fatal or severe crashes is indicated to occur with low street network density (). Additionally, a relevant study about the effect of street pattern on non-motorist crashes shows loops and lollipops increases the probability of an injury for pedestrians and bicyclists but reduces the probability of fatality and property-damage-only in an event of a crash (0). However, others have found that no connectivity factors are statistically significant for vehicle or pedestrian crashes (). Considering the widely accepted effects of connectivity on non-motorists travel and the vary conclusions about safety impacts for all road users, it is appropriate to start from the investigation of association between connectivity and pedestrian-bicyclist accidents.. Study Objectives Based on the review of past researches, connectivity is well measured and widely accepted to have impacts on nonmotorized road users, but the safety performance of road networks with different connectivity levels is still not clear. Even though the effects of many factors on pedestrian-bicyclist accidents have been explored, little work has been done to examine the factors about connectivity. Moreover, the previous studies draw different conclusions about street connectivity and its impact on all-road-user safety. Considering the stated issues, the aims of this paper are: ) to focus on the association between connectivity and pedestrian-bicyclist accidents; )to examine in how the connectivity impacts safety and; ) to determine which

4 ZHANG, BIGHAM, LI, RAGLAND, and CHEN connectivity measure has strong impacts on safety. Toward these goals, this paper analyzes data from Alameda County, California, at the census tract level which is a proper unit for non-motorized travel study (). A spatial statistical model called geographically weighted regression (GWR) is utilized to evaluate the relationship between each connectivity measure and non-motorist accidents. Within these models, other factors are all included, like travel behavior, transportation facilities, demographic features, and land use. DATA. Data Source There are five categories of data employed in this paper, extracted from the road networks, crash records, census statistics, and traffic forecasting models available for Alameda County, California. All the data are calculated and aggregated by census tracts because: first, the median size of census tracts in Alameda County resembles a proper area for walking (trips are typically under one mile) and cycling (trips are typically under miles) (), with the third quartile value of tract size as.0 square miles and % tracts are under. square miles; traffic analysis zones and other spatial units are either too small or too large; second, there are census tracts, which is a proper sample size for GWR models (). All the data in the study are collected for the same period of time when possible, except the census data which is from the year 000. However, it is the closet time to satisfy other data and the population structure is always assumed to have not changed much. Crash Data Road accidents involving pedestrians and bicyclists in Alameda County, CA from 00 to 00 are analyzed in this research. The crash data is from Transportation Injury Mapping System (TIMS) which was established by researchers at the Safe Transportation Research and Education Center (SafeTREC) at the University of California, Berkeley. TIMS provides data based on crash records from Statewide Integrated Traffic Records System (SWITRS), and offers mapping analysis tools and information for traffic safety related research, policy and planning. All the crashes are already geocoded on the road network. Travel Behavior Data Travel behavior data are collected to describe traffic condition from two angles: the first is to use vehicle miles traveled (VMT) to reflect the traffic intensity of the road network in each census tract. This data is obtained from Bay Area Simplified Simulation of Travel, Energy and Greenhouse Gases model for 00, already aggregated by traffic analysis zones and census tracts. Then, the numbers of workers using private vehicles or public transportation or non-motorized means are applied to show the travel mode choices of each area. These data are obtained from U.S. Census 000 data from the U.S. Census Bureau website. Land Use Data Shown to have significant impacts on non-motorized travel (), number of commercial unit and house unit in each census tract are selected to control the land use impact. The commercial data is from the Alameda County pedestrian intersection crossing volume model (ACPICVM) established by SafeTREC. The house unit data is directly from the Census 000 data. The year structure built data are also included in the analysis to reflect the age of an area, calculated as how many house units are built before 0, because it is indicated in a research that an area built before 0 s has a different safety performance than areas built in more recent times (). Demographic Data The populations aged from 0 to, to, and and older are employed to show the population structure; median household income and employment rate are chosen to reflect the economy condition. All these data are from the Census 000 data. Transportation Facility Data The numbers of bus lines in each census tract are aggregated to reflect the transit accessibility, and this is also from the ACPICVM mentioned above. Additionally, -way, -way, more-than--way intersection numbers, and connectivity measures are calculated based on the road network which is derived from ESRI StreetMap North America. Because this paper focuses on the pedestrian-bicyclist crashes, all the primary highway road lines with limited access are excluded.

5 ZHANG, BIGHAM, LI, RAGLAND, and CHEN 0. Selection of Connectivity Measures A wide range of measures of connectivity has been drawn from different fields, such as urban planning (; ), urban design (0), transportation (), and landscaping (). Along with the availability of computer and spatial data, network science offers many topological indexes which are more complex but can reveal the basic connections between road links (). Based on the review of these studies, measures can be summarized into three categories: metric, topological, and behavioral, as shown in table. TABLE Measures of Connectivity in Previous Research Category Measure Definition Metric (Size-length Block length Measured from the curb or from the centerline of the street intersection. related) Block size Measured by the width and length, the area, or the perimeter. Block density The mean number of census blocks per square mile. Intersection The number of intersections per square mile. density Street density The number of linear miles of streets per square mile of defined area. Topological (nodelink Connected node The number of street intersections divided by the number of related) ratio intersections plus cul-de-sacs. Link-node ratio The number of links divided by the number of nodes within a defined area. Grid or not To categorize whether a network is a grid or not. Gamma index The ratio of the number of links in the network to the maximum possible number of links between nodes. Alpha index The ratio of the number of actual circuits to the maximum number of circuits. Treeness or The percentage a network consists of circuit-like element. circuitness Nodal A comprehensive assessment of network connectivity combined connectivity topological as well as metric perspective. index Behavioral Route directness The ratio of route distance to straight-line distance for two selected (walking-route points. related) Walking distance The maximum, mean or minimum route distances from homes to Effective walking area potential destinations within a defined area. The ratio of the number of parcels within a one-quarter mile walking distance of a node to the total number of parcels within a one-quarter mile radius of that node. Reach The total street length covered by all paths extending out from that point that are no longer than a given threshold value Directional The minimum number of direction changes required to reach any part of distance the network from that point This paper focuses on measures which are more widely accepted, clearly defined, and especially policy friendly. Thus measures were chosen: block density, intersection density, street density, and mean block length. These measures reflect the connectivity from a metric dimension perspective, showing the quality of connection between road links considering both the quantity of road system and the size of the analysis unit. Mean block length is widely used in street design and community planning standards and in this paper it is the average value of all block lengths measured from the centerline of the street intersections in each census tract; block density defines blocks as areas of land surrounded by roads; intersection density includes all the nodes in a road network except the dead-end nodes. These measures are easy to understand since they are directly related with the dimension of the length and width of roads and the size of areas. The connectivity represented by these measures varies across the census tracts in Alameda County, as shown in figure (a), (d), (g), and (j).

6 ZHANG, BIGHAM, LI, RAGLAND, and CHEN (a) (b) (c) FIGURE Distribution of the connectivity measure value and the estimation results: (a) distribution of block density (blocks/mi ), (b) parameter distribution of block density, and (c) t value distribution of block density.

7 ZHANG, BIGHAM, LI, RAGLAND, and CHEN (d) (e) (f) FIGURE (continued) Distribution of the connectivity measure value and the estimation results: (d) distribution of intersection density (intersections/mi ), (e) parameter distribution of intersection density, and (f) t value distribution of intersection density.

8 ZHANG, BIGHAM, LI, RAGLAND, and CHEN (g) (h) (i) FIGURE (continued) Distribution of the connectivity measure value and the estimation results: (g) distribution of street density (mi/mi ), (h) parameter distribution of street density, and (i) t value distribution of street density.

9 ZHANG, BIGHAM, LI, RAGLAND, and CHEN (j) (k) 0 (l) FIGURE (continued) Distribution of the connectivity measure value and the estimation results: (j) distribution of mean block length (ft), (k) parameter distribution of mean block length, and (l) t value distribution of mean block length.

10 ZHANG, BIGHAM, LI, RAGLAND, and CHEN STATISTICAL MODELS Along with the development of statistics, different statistical models are employed to quantify the relationship between road network features and crash occurrence. The crash data is a type of count data exhibiting overdispersion so negative binomial regression models have been widely employed to evaluate the association between urban forms and crashes (). This common technique assumes a spatial stationarity in the relationship between collision count and contributing factors. Under this assumption, fixed coefficients are estimated to represent all the different analysis units for the entire study area, assuming the relationship between dependent variable and independent variables does not vary across the geographic area. However, this stationary relationship may be broken when applying to crash analysis. Safety performance is likely influenced by many factors which are spatially defined and related between continuous areas such as census tracts, traffic analysis zones, or census blocks. These factors could be land use, demographic features, and road networks, which could be strong predictors at some locations but weak at others (). For example, when the relationship between crashes and intersection numbers is estimated for each census block in a region, the estimation result could be different across census blocks with different income level. For low income level locations, more intersections could expose cars in more conflicts, thus there could be more crashes. However in other locations with higher income level, the number of intersections may not have significant impact on crashes because residents with high income can afford expensive vehicles which have better safety protection potentially offseting the increase of crashes. As a result, ignoring the spatial non-stationarity between crashes and spatial related factors could lead to the inaccuracy of model findings.. Introduction to Geographically Weighted Regression To address the non-stationarity problem mentioned above, geographically weighted regression (GWR) has been developed to allow relationships between dependent and independent variables to vary across locations (). Consider a regular regression model written as: () Where y is the dependent variable observed in location i; β is the interception; k is the total number of independent variables; β is the parameter of the kth independent variable; x is the kth independent variable observed in location i; is the error term for the estimation in location i. β is estimated globally and do not change with locations so that this model is called global model. GWR allows local rather than global parameters β to be estimated by extending this traditional regression framework as:,, () Where u,v denotes the coordinates of the ith location point (census tract centroid in this study) in the study area; β u,v is a realization of the continuous function β u, v at location I, so GWR models can be called local models compared to the traditional ones. In this way the GWR recognizes the existence of spatial variations in relationships and calibrates the equation in a reasonable way weighted regression. For the purpose of this paper is not to introduce the calibration of GWR, detailed information about calibration could be found in relative research (), and the calculation in this paper will be finished using a software called GWR.0 ().. Model Specification Model Form The basic GWR assume a normally distributed error structure in the calibration of the regression model. This assumption is not upheld when calibrating models for count data so a Poisson distribution is thus more appropriate. Although a negative binomial distribution is better than the Poisson distribution because of the over-dispersion of crash data, the use of Poisson regression does not produce inaccurate estimates (). Furthermore, considering the availability of Poisson regression for GWR.0 software utilized in this study, the model of this study is developed using Poisson distribution form as:,,,, () Where Exposure is the exposure variable in Poisson regression model; others are the same as mentioned above.

11 ZHANG, BIGHAM, LI, RAGLAND, and CHEN 0 Variables The dependent variable is the average crashes involving pedestrian and bicyclist per year. It is calculated as the mean of the crashes from 00 to 00 to minimize the data fluctuation through years. The independent variables are classified into five categories: connectivity measures, land use, travel behavior, transportation facilities, and demographic features, as shown in table. This paper chooses population density instead of VMT as exposure variable because previous research at the TAZ level indicates that VMT does not perform well as exposure when the study unit is a continuous area rather than individual facility. Also, as one of the widely used exposures, population density also can reflect strong positive relationship with traffic crashes especially for regional study (). TABLE List of Variables and the Descriptive Analysis Category Variable Symbol Avg Min Max S.D. Number of crashes involving pedestrians and bicyclists for Crashes each census tracts per year (based on crashes from 00-00) Block density (blocks/ mile ) BlkDen Intersection density Connectivity (intersections/ mile IntDen ) measures Street density (miles/ mile ) StDen Mean block length (feet) MBlkLen Number of Commercial properties in the census tract ComCnt Land use Number of housing units in the census tract HUTot Rate of house units built before 0 E Vehicle miles traveled TotVMT...0. Number of workers years and over who go to work using WTPRV private vehicle Travel behavior Number of workers years variables and over who go to work using WTPUB public transportation Number of workers years and over who go to work using WTBW biking or walking Number of bus lines in the census tract BusCnt Transportation facility variables Demographic variables Number of way intersections in the census tract WayIntCnt Number of way intersections in the census tract WayIntCnt Number of more-than--way intersections in the census tract MWayIntCnt Population density (persons/mile ) PD Population age 0 to Pop Population age to Pop_ Population age and older Pop Employment rate EmR Average household income in HHInc

12 ZHANG, BIGHAM, LI, RAGLAND, and CHEN 0 0 Model Structures Considering the co-linearity between different connectivity measures, all connectivity measures will be employed separately in series of models. And because there are so many independent variables that a forward procedure is used in this paper to test which variables should be included in a model to make the best estimation (). In this procedure, a simple model with only a connectivity measure, an exposure variable, and an intercept term is used as a starting point. Then, other control variables will be added to the model one category by one category. This procedure produces models. In each model, there will be one connectivity measure, together with other control variables, as shown in table. Also, prior to incorporating variables into the same model, a correlation test has been conducted to examine whether variables are highly correlated with each other. If two variables are substantially correlated, they will not be inserted into the same model. TABLE Model Structure Category Dependent variable Exposure variable Connectivity measures Land use variables Travel behavior variables Transportation facility variables Demographic variables RESULTS Variable Selection of Variables in models # # # # # # # # # #0 # # # # # Ln y Ln PD BlkDen Or IntDen Or StDen Or MBlkLen ComCnt HUTot E0 TotVMT WTPRV WTPUB WTBW BusCnt WayIntCnt WayIntCnt MWayIntCnt Pop Pop_ Pop EmR HHInc. Parameter Estimation for Connectivity Measures GWR calibrates local models for each location, so that the results of the GWR models are a set of local parameters for each independent variable. Therefore, each variable will have estimations for parameter, t value, and standard error, varying across census tracts. Focusing on the impacts of connectivity, the parameters for each connectivity measure in different models are summarized in table. Since each connectivity measure can have parameters estimated in each model, the parameters are presented in the order of the minimum, the lower quartiles, the median quartiles, the upper quartiles, and the maximum values from top to bottom in each cell in table.

13 ZHANG, BIGHAM, LI, RAGLAND, and CHEN TABLE Parameter Estimations for Connectivity Measures in Different Models Connecti Parameters estimated for each connectivity measure in model # to # vity # # # # # # # # measure Block density Intersecti on Density Street Density Mean block length Connecti vity measure Block density Intersecti on Density Street Density Mean block length Parameters estimated for each connectivity measure in model # to # # #0 # # # # # Significance Level The t statistic value for each parameter also varies across census tracts as shown in figure, so there is no single t value to represent the significant level of the estimation as it is expected in the regular global regression. Thus, this paper develops the significance rate value calculated as the rate of census tracts whose parameter estimations for connectivity have t value bigger than. or smaller than -. (±. is the critical value for two tailed t-test at the confidence level of 0% for degree of freedom of 0) to show how many census tracts have statistically significant relationship between connectivity measures and non-motorist accidents at 0% confidence level. The

14 ZHANG, BIGHAM, LI, RAGLAND, and CHEN 0 0 significance rate for parameter estimation of each connectivity measure is shown in figure along the horizontal axis titled as level of significance. This paper assume that an estimation is fair if the significance rate is higher than 0.. Therefore, the points on the right side of the black vertical line represent models which have better ability to estimate parameters at 0% confidence level.. Better-fit models Regarding how effectively the model can describe the relationship, this paper introduces the Corrected Akaike Information Criterion (AICc) (): a lower value of AICc indicates a better fit model. The AICc values for all the models are displayed in figure, from the minimum value of to the maximum value of. This paper assumes that the AICc value lower than 0 indicates a better-fit model. As can be seen in figure, the points under the black horizon line represent models which have better fitness. In order to further study the associations between each connectivity measure and non-motorist accidents, models which can effectively describe the relationship with more significant estimations should be selected for analysis. Therefore, both the significance level and goodness of fit are considered together. First, the models with AICc value lower than 0 are focused on. Then, among these lower AICc models, those for each connectivity measures with the highest significance rate are picked out for further analysis: model No. with the connectivity measure of block density; model No. with the connectivity measure of intersection density; model No. with the connectivity measure of mean block length; and model No. with the connectivity measure of street density, as shown in bolded cells in table and points circled out in figure AICc value block density intersection density street density mean block length Level of significance Figure The significance level of parameter estimation for connectivity measures and goodness of fit for different models.

15 ZHANG, BIGHAM, LI, RAGLAND, and CHEN CONCLUSIONS According to the significance rate and model fitness level, models are chosen to examine the associations between connectivity and pedestrian-bicyclist accidents.. Positive Or Negative? The Direction of Impact The GWR results show the parameter of a certain variable is not constant for all census tracts. Thus the parameter for connectivity measure will vary across different census tracts, as shown in figure. Figure (b) shows the parameter distribution of block density among all the census tracts, with negative values in most tracts except the tracts shown in white in the north central area. On the surface, it appears that the relationship between block density and pedestrian-bicyclist crashes could be negative in some tracts and positive in others. However, the conclusion is different if the t value distribution is examined further. As mentioned above, the t values are different for a variable in different locations. Shown in figure (c), the t values for block density vary across the census tracts. The area with t value bigger than -. provides an insignificant relationship between block density and pedestrian-bicyclist crashes. It is obvious that the tracts with positive parameters in figure (b) are also the tracts with insignificant relationships in figure (c), so the positive estimation can be ignored. Thus, a significant negative relationship can be concluded between block density and pedestrian-bicyclist crashes, which means larger block density is related to a decrease of non-motorist crashes. In figure (e), the parameter estimations for intersection density are all negative values that range from to It can be concluded that for all census tracts, intersection density has a negative relationship with pedestrian-bicyclist crashes: the denser the intersections, the safer for pedestrians and bicyclists. This is confirmed in figure (h), where the same type of association is seen: dense street leads to fewer non-motorist crashes. On the contrary, the signs of parameters for mean block length are all positive across the study area. As shown in figure (k), the minimum value of the parameter is 0.000, and the maximum value is This indicates that the shorter mean block length has a significant relationship with a safer environment for pedestrians and bicyclists.. Weak or Strong? The Degree of Impact The parameters estimated by GWR not only vary in signs, but also change in quantities, which indicates the degree of the influence of a variable can be different across tracts. The relationship could be strong in some census tracts but be weak in others. Regardless of whether the sign is negative or positive, the absolute values of a parameter show how strong the connectivity measure is related to pedestrian-bicyclist safety. As shown in figure (b), the parameters of block density in the west part of Alameda County can be very close to 0, which means that the influence of block density on safety can be very weak for these tracts. When one unit of increase happens to the block density, no noticeable change could be expected for accident number. However, every conclusion should be drawn at certain confidence level. Comparing the t value distribution in figure (c), it is easy to find that the small absolute values in figure (b) are all the tracts with t values bigger than -. which means insignificant estimation at 0% confidence level. In other words, in highly significant tracts the absolute values of parameters are relatively large. The same results can be obtained from figure (e) to (l), indicating that the intersection density, the street density, and the mean block length all strongly influence pedestrian-bicyclist safety at 0% confidence level.. The Better Measure and the Better Model The street density is a better measure among all the four measures for estimating associations between connectivity and pedestrian-bicyclist safety because of several reasons: first, the parameters estimated for street density have relatively higher significance rate as 0. shown in figure, which means % census tracts have 0% level of confidence to explain the relationship; second, the model including street density has the smallest AICc value, which means that this model is the best fit model among the four models; third, the variance range for parameters of street density is from -0.0 to -0.0, with the variance rate of 0., while the other connectivity measures are varying by the rate of 0. for block density,.0 for intersection density, and 0.0 for mean block length. So the street density has a more stable influence on pedestrian-bicyclist crashes. If a better model should be recommended for analyzing connectivity and pedestrian-bicyclist safety, the model No. is a good choice. First, this model includes not only the connectivity measures but also the travel behavior and transportation facility factors which are employed in previous research and proved to have great impacts on traffic safety. Secondly, all the four connectivity measures have a higher significant rate in this model than in other models, which means that this model can significantly explain the relationship for more census tracts; Finally, the AICc values for this model for all the four connectivity measures are all relatively small, leading to the conclusion that this is a better fit model.

16 ZHANG, BIGHAM, LI, RAGLAND, and CHEN Summary Based on the stated conclusions, it is clear that higher connectivity means safer environment for pedestrians and bicyclists. Higher block density would indicate more and smaller blocks surrounded by streets; denser intersections would mean more connections between streets in a unit of area; larger street density would represent more streets; shorter mean block length would define an area with frequent intersections. While all these measures can lead to a higher connectivity and a decrease in pedestrian-bicyclist accidents, the street density can provide the most explanatory, the best fit and the most stable estimation. Additionally, employing factors of street network, travel behavior, and transportation facility in a GWR model could provide better estimation results and a higher goodness of fit. DISCUSSION This paper estimates the associations between connectivity and pedestrian-bicyclist accidents, and determines that higher connectivity could relate to fewer crashes for non-motorized road users. Some of the previous studies have different opinions, arguing that more through vehicle traffic could form more conflicts between cars and nonmotorists and thus lead to more crashes. However, the findings of this paper provide a possibility to further understand the relationship between connectivity and safety. It is widely accepted that higher connectivity may lead to more traffic on local roads, but smaller block length and frequent intersections can slow down the traffic speed and force the drivers to focus on driving. Higher connectivity can attract more walking and bicycling travel, which can lead to more pedestrians and bicycles along the cars that can also force drivers to be more careful. Moreover, by shifting people in cars to walk and bike, the overall number of vehicles on the road could be reduced to increase safety of non-motorists. All these considerations can explain why higher connectivity could relate to fewer pedestrian-bicyclist crashes. Several improvements should be conducted in future studies: other connectivity measures like link node ratio and walking distance should be included in the model since these measures show different characteristics of connectivity; a negative binomial distribution in GWR could be used to overcome the over-dispersion problem of crash count data; Alameda county has roads with widely varied slope grades which can greatly influence traffic mode choice, so terrain factors like slope grade of roads should be employed in the model as control variables; for further understanding of the safety performance of road connectivity, crashes for motorized users and different severity level should also be studied. Well-connected road patterns like rectilinear grid network emerged as a rational way to subdivide land and provide circulation for pedestrians and goods. Growing use of the automobile prompted a move away from this traditional pattern to a more curvilinear cul de sac preferred network which thought to be safer and quieter by limiting through traffic. But this pattern is gradually recognized to reduce connectivity within the neighborhood, leading to indirect, inefficient routes between locations, and less walking and biking. As a result, the well-connected grid like pattern has been again advocated by planners. Before hurriedly turning back, planners and policy makers should know more about connectivity and its effects. This paper studies the safety performance of road network connectivity, including not only connectivity measures but also other control variables like travel behavior, land use, transportation facility, and demographic features into an advanced spatial statistical model. The use of the spatial model and effective control factors could provide more accurate results. The street density and the model structured by connectivity measures, travel behavior factors, and transportation facility factors are recommended. The findings could be helpful for developing policy guidelines that consider safety as an element of level of service. REFERENCE. Jennifer Dill. Measuring Network Connectivity for Bicycling and Walking. Presented at rd Annual Meeting of the Transportation Research Board, Washington, D.C., 00.. J Michael Oakes, Ann Forsyth, and Kathryn H Schmitz. The Effects of Neighborhood Density and Street Connectivity on Walking Behavior: The Twin Cities Walking Study. Epidemiologic Perspectives & Innovations, Vol., No., 00, pp. -.. David Berrigan, Linda W Pickle, and Jennifer Dill. Associations between Street Connectivity and Active Transportation. International Journal of Health Geographics, Vol., No. 0, 00, pp. -.. Mohamed Abdel-Atya, Sai Srinivas Chundia and Chris Lee. Geo-Spatial and Log-Linear Analysis of Pedestrian and Bicyclist Crashes Involving School-Aged Children. Journal of Safety Research, Vol., No., 00, pp. -.

17 ZHANG, BIGHAM, LI, RAGLAND, and CHEN Eluru N, Bhat CR, and Hensher DA. A Mixed Generalized Ordered Response Model for Examining Pedestrian and Bicyclist Injury Severity Level in Traffic Crashes. Accident Analysis and Prevention, Vol. 0, No., 00, pp Kim JK, Kim S, Ulfarsson GF et al. Bicyclist Injury Severities in Bicycle-Motor Vehicle Accidents. Accident Analysis and Prevention, Vol., No., 00, pp. -.. Macpherson AK, To TM, Parkin PC et al. Urban/Rural Variation in Children s Bicycle-Related Injuries. Accident Analysis and Prevention, Vol., No., 00, pp. -.. J N Ivan, S S Zajac, and P Garder. Finding Strategies to Improve Pedestrian Safety in Rural Areas. Cambridge, Mass.: New England University Transportation Center, Massachusetts Institute of Technology: Springfield, Va.: National Technical Information Service, 00.. Sullivan, J. M., and Flannagan, M J. The Role of Ambient Light Level in Fatal Crashes: Inferences from Daylight Saving Time Transitions. Accident Analysis and Prevention, Vol., No., 00, pp Rifaat SM, Tay R, and de Barros A. Effect of Street Pattern on the Severity of Crashes Involving Vulnerable Road Users. Accident Analysis and Prevention, Vol., No., 0, pp. -.. Shakil Mohammad Rifaat, Richard Tay, and Alex de Barros. Effect of Street Pattern on Road Safety: Are Policy Recommendations Sensitive to Aggregations of Crashes by Severity? In Transportation Research Record: Journal of the Transportation Research Board, No., Transportation Research Board of the National Academies, Washington, D.C., 00, pp. -.. Ewing R, Schieber RA, and Zegeer CV. Urban Sprawl as A Risk Factor In Motor Vehicle Occupant And Pedestrian Fatalities. American Journal of Public Health, Vol., No., 00, pp. -.. Pawlovich, M., Li, W., Carriquiry, Alicia et al. Iowa s Experience with Road Diet Measures: Use of Bayesian Approach to Assess Impacts on Crash Frequencies and Crash Rates. In Transportation Research Record: Journal of the Transportation Research Board, No., Transportation Research Board of the National Academies, Washington, D.C., 00, pp. -.. Wesley E. Marshall, and Norman W. Garrick. Street Network Types and Road Safety: A Study of California Cities. Urban Design International, Vol., No., 00, pp. -.. Kristie Werner Gladhill. Exploring Traffic Safety and Urban Form in Portland. Thesis for Master Degree, Portland State University, 00.. A. Stewart Fotheringham, Chris Brunsdon, Martin Charlton. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons Ltd, England, 00.. Robert J. Schneider, Lindsay S. Arnold, and David R. Ragland. Validation Testing and Refinement of the Alameda County Pedestrian Intersection Crossing Volume Model White Paper. Association of Collegiate Schools of Planning Conferenc, 00.. Amrosell. Improve Safety By Increasing Street Connectivity. July 0.. Susan Handy, Robert G. Paterson, and Kent Butler. Planning for Street Connectivity: Getting from Here to There. Planning Advisory Service Report, American Planning Association Cervero, R. and Kockelman, K. Travel Demand and the Ds: Density, Diversity, and Design. Transportation Research Part D, Vol.,, pp. -.. Jan Scheurer, and Sergio Porta. Centrality and Connectivity in Public Transportation Networks and their significance for Transport Sustainability in Cities. In: World Planning Schools Congress, Global Planning Association Education Network, to 00-0-, Mexico.. TISCHENDORF Lutz, FAHRIG Lenore, How Should We Measure Landscape Connectivity. Landscape Ecology, Vol., No., 000, pp. -.. Yuanyuan Zhang, Xuesong Wang, Peng Zeng et al. Centrality Characteristics of Traffic Analysis Zone Road Network Patterns. Accepted by Transportation Research Record: Journal of the Transportation Research Board, 0.. Alireza Hadayeghi, Amer S. Shalaby, Bhagwant N. Persaud. Development of Planning Level Transportation Safety Tools Using Geographically Weighted Poisson Regression. Accident Analysis and Prevention, Vol., 00, pp. -.. GWR.0 Software Package. Martin Charlton, Stewart Fotheringham, Chris Brunsdon. Spatial Analysis Research Group. Department of Geography, University of Newcastle upon Tyne, Newcastle upon Tyne, ENGLAND, 00.. Miaou, S.P. The Relationship between Truck Accidents and Geometric Design of Road Sections: Poisson Versus Negative Binomial Regressions. Accident Analysis and Prevention, Vol., No.,, pp. -.. Felipe Ladron de Guevara, Simon P. Washington, and Jutaek Oh. Forecasting Crashes at the Planning Level: Simultaneous Negative Binomial Crash Model Applied in Tucson, Arizona. In Transportation Research Record:

18 ZHANG, BIGHAM, LI, RAGLAND, and CHEN Journal of the Transportation Research Board, No., Transportation Research Board of the National Academies, Washington, D.C., 00, pp. -.