Predicting road system speeds using spatial structure variables and network characteristics

Spatial regression is applied to GPS floating car measurements to build a predictive model of road system speed as a function of link type, time period, and spatial structure. The models correct for correlated spatial errors and autocorrelation of speeds. Correlation neighborhoods are based on either Euclidean or network distance. Econometric and statistical methods are used to choose the best model form and statistical neighborhood. Models of different types have different coefficient estimates and fit quality, which might affect inferences. Speed predictions are validated against a holdout sample to illustrate the usefulness of spatial regression in road system speed monitorin

Predicting Segment-Intersection Crashes with Land Development Data

Experience with crash prediction modeling has confirmed the importance of traffic volume, not only as exposure but also as a predictive variable. For intersection-related collisions, for example, angle collisions or any collisions involving turning vehicles, traffic volumes on both intersecting roads are necessary for sufficient prediction of crash count These collisions occur not only at intersections but any place where vehicles turn on or off the roadway, such as driveways. Intersecting traffic volumes at such locations are either not available or labor intensive to acquire. The objective of this study was to investigate the use of geographic information system (GIS) land use inventories to supplement observed traffic volumes as exposure measures for estimating models for predicting segment-intersection crashes, defined as collisions occurring on road segments involving one or more turning or crossing vehicles. Model results for rural two-lane and urban two- and four-lane undivided roads indicate that the number of trips generated and the extent of surrounding land development itself act as excellent predictors for segment-intersection crashes and in fact work better than models using the number of access points. The reason is that those variables better describe the intensity of the traffic accessing the major artery. This is a valuable finding, since access points along a road segment cannot be counted automatically, but many jurisdictions have GIS land use inventories available for all sorts of planning purposes. Such a development will permit better accounting of exposure to segment-intersection crashes in crash prediction modeling

Network-Based Highway Crash Prediction Using Geographic Information Systems

The objectives of this project were to estimate network-based crash prediction models that will predict the expected crash experience in any given geographic area as a function of the highway link, intersection and land use features observed in the area. The result is a system of GIS programs that permit a polygon to be drawn on a map, or a set of links and intersections to be selected, and then predict the number of crashes expected to occur on the selected traffic facilities. These expected values can then be compared with observed values to identify locations with higher than usual crash incidence and may require attention to improve the safety of the location. Alternatively, this tool could be used to estimate the safety impacts of proposed changes in highway facilities or in different land development scenarios. A network approach was chosen to solve this problem, in which separate models were estimated for crashes at major intersections, and intersection-related and segment-related crashes on road segments. All three sets of models can then be used to predict the number of crashes for an entire highway facility delineated as the user desires – including all intersections. These models also consider all relevant road features, in particular the intensity of traffic at intersections and driveways resulting from the surrounding land use. Gathering traffic volumes at every intersection and driveway on the road network would preclude the feasibility of such an approach, both for estimation and in practice. Instead, the link between land development and trip generation was exploited to estimate the driveway and minor road volumes. Land development intensity variables were generated from land use inventories organized using Geographic Information Systems (GIS), permitting virtually automatic preparation of the required data sets for model estimation and application and prediction of crash counts on roads. Specifically, population and retail and non-retail employment counts were associated with each analysis segment to represent vehicle exposure to intersection-related crashes. GIS was used for two purposes in this project: 1) distributing population and employment counts in a traffic analysis zone (TAZ) among all the links in that zone. 2) Visually comparing the predicted and observed accident counts in order to identify higher than usual crash locations

Predicting road system speeds using spatial structure variables and network characteristics

ISSN:1435-5930ISSN:1435-594