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SPATIO-TEMPORAL ANALYSES FOR PREDICTION OF TRAFFIC FLOW, SPEED AND OCCUPANCY ON I-4
Title
SPATIO-TEMPORAL ANALYSES FOR PREDICTION OF TRAFFIC FLOW, SPEED AND OCCUPANCY ON I-4.
Creator
Chilakamarri Venkata, Srinivasa Ravi Chandra, Al-Deek, Haitham, University of Central Florida
Abstract / Description
Traffic data prediction is a critical aspect of Advanced Traffic Management System (ATMS). The utility of the traffic data is in providing information on the evolution of traffic process that can be passed on to the various users (commuters, Regional Traffic Management Centers (RTMCs), Department of Transportation (DoT),…etc) for user-specific objectives. This information can be extracted from the data collected by various traffic sensors. Loop detectors collect traffic data in the form of...
Show moreTraffic data prediction is a critical aspect of Advanced Traffic Management System (ATMS). The utility of the traffic data is in providing information on the evolution of traffic process that can be passed on to the various users (commuters, Regional Traffic Management Centers (RTMCs), Department of Transportation (DoT),…etc) for user-specific objectives. This information can be extracted from the data collected by various traffic sensors. Loop detectors collect traffic data in the form of flow, occupancy, and speed throughout the nation. Freeway traffic data from I-4 loop detectors has been collected and stored in a data warehouse called the Central Florida Data Warehouse (CFDWTM) by the University of Central Florida for the periods between 1993 – 1994 and 2000 - 2003. This data is raw, in the form of time stamped 30-second aggregated data collected from about 69 stations over a 36 mile stretch on I-4 from Lake Mary in the east to Disney-World in the west. This data has to be processed to extract information that can be disseminated to various users. Usually, most statistical procedures assume that each individual data point in the sample is independent of other data points. This is not true to traffic data as they are correlated across space and time. Therefore, the concept of time sequence and the layout of data collection devices in space, introduces autocorrelations in a single variable and cross correlations across multiple variables. Significant autocorrelations prove that past values of a variable can be used to predict future values of the same variable. Furthermore, significant cross-correlations between variables prove that past values of one variable can be used to predict future values of another variable. The traditional techniques in traffic prediction use univariate time series models that account for autocorrelations but not cross-correlations. These models have neglected the cross correlations between variables that are present in freeway traffic data, due to the way the data are collected. There is a need for statistical techniques that incorporate the effect of these multivariate cross-correlations to predict future values of traffic data. The emphasis in this dissertation is on the multivariate prediction of traffic variables. Unlike traditional statistical techniques that have relied on univariate models, this dissertation explored the cross-correlation between multivariate traffic variables and variables collected across adjoining spatial locations (such as loop detector stations). The analysis in this dissertation proved that there were significant cross correlations among different traffic variables collected across very close locations at different time scales. The nature of cross-correlations showed that there was feedback among the variables, and therefore past values can be used to predict future values. Multivariate time series analysis is appropriate for modeling the effect of different variables on each other. In the past, upstream data has been accounted for in time series analysis. However, these did not account for feedback effects. Vector Auto Regressive (VAR) models are more appropriate for such data. Although VAR models have been applied to forecast economic time series models, they have not been used to model freeway data. Vector Auto Regressive models were estimated for speeds and volumes at a sample of two locations, using 5-minute data. Different specifications were fit – estimation of speeds from surrounding speeds; estimation of volumes from surrounding volumes; estimation of speeds from volumes and occupancies from the same location; estimation of speeds from volumes from surrounding locations (and vice versa). These specifications were compared to univariate models for the respective variables at three levels of data aggregation (5-minutes, 10 minutes, and 15 minutes) in this dissertation. For data aggregation levels of <15 minutes, the VAR models outperform the univariate models. At data aggregation level of 15 minutes, VAR models did not outperform univariate models. Since VAR models were used for all traffic variables reported by the loop detectors, this made the application of VAR a true multivariate procedure for dynamic prediction of the multivariate traffic variables – flow, speed and occupancy. Also, VAR models are generally deemed more complex than univariate models due to the estimation of multiple covariance matrices. However, a VAR model for k variables must be compared to k univariate models and VAR models compare well with AutoRegressive Integrated Moving Average (ARIMA) models. The added complexity helps model the effect of upstream and downstream variables on the future values of the response variable. This could be useful for ATMS situations, where the effect of traffic redistribution and redirection is not known beforehand with prediction models. The VAR models were tested against more traditional models and their performances were compared against each other under different traffic conditions. These models significantly enhance the understanding of the freeway traffic processes and phenomena as well as identifying potential knowledge relating to traffic prediction. Further refinements in the models can result in better improvements for forecasts under multiple conditions.
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Date Issued
2009
Identifier
CFE0002593, ucf:48276
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002593
ESTIMATION OF HYBRID MODELS FOR REAL-TIME CRASH RISK ASSESSMENT ON FREEWAYS
Title
ESTIMATION OF HYBRID MODELS FOR REAL-TIME CRASH RISK ASSESSMENT ON FREEWAYS.
Creator
pande, anurag, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description
Relevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would...
Show moreRelevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would involve anticipating incidents such as crashes. The primary element of proactive traffic management strategy would be model(s) that can separate 'crash prone' conditions from 'normal' traffic conditions in real-time. The aim in this research is to establish relationship(s) between historical crashes of specific types and corresponding loop detector data, which may be used as the basis for classifying real-time traffic conditions into 'normal' or 'crash prone' in the future. In this regard traffic data in this study were also collected for cases which did not lead to crashes (non-crash cases) so that the problem may be set up as a binary classification. A thorough review of the literature suggested that existing real-time crash 'prediction' models (classification or otherwise) are generic in nature, i.e., a single model has been used to identify all crashes (such as rear-end, sideswipe, or angle), even though traffic conditions preceding crashes are known to differ by type of crash. Moreover, a generic model would yield no information about the collision most likely to occur. To be able to analyze different groups of crashes independently, a large database of crashes reported during the 5-year period from 1999 through 2003 on Interstate-4 corridor in Orlando were collected. The 36.25-mile instrumented corridor is equipped with 69 dual loop detector stations in each direction (eastbound and westbound) located approximately every ½ mile. These stations report speed, volume, and occupancy data every 30-seconds from the three through lanes of the corridor. Geometric design parameters for the freeway were also collected and collated with historical crash and corresponding loop detector data. The first group of crashes to be analyzed were the rear-end crashes, which account to about 51% of the total crashes. Based on preliminary explorations of average traffic speeds; rear-end crashes were grouped into two mutually exclusive groups. First, those occurring under extended congestion (referred to as regime 1 traffic conditions) and the other which occurred with relatively free-flow conditions (referred to as regime 2 traffic conditions) prevailing 5-10 minutes before the crash. Simple rules to separate these two groups of rear-end crashes were formulated based on the classification tree methodology. It was found that the first group of rear-end crashes can be attributed to parameters measurable through loop detectors such as the coefficient of variation in speed and average occupancy at stations in the vicinity of crash location. For the second group of rear-end crashes (referred to as regime 2) traffic parameters such as average speed and occupancy at stations downstream of the crash location were significant along with off-line factors such as the time of day and presence of an on-ramp in the downstream direction. It was found that regime 1 traffic conditions make up only about 6% of the traffic conditions on the freeway. Almost half of rear-end crashes occurred under regime 1 traffic regime even with such little exposure. This observation led to the conclusion that freeway locations operating under regime 1 traffic may be flagged for (rear-end) crashes without any further investigation. MLP (multilayer perceptron) and NRBF (normalized radial basis function) neural network architecture were explored to identify regime 2 rear-end crashes. The performance of individual neural network models was improved by hybridizing their outputs. Individual and hybrid PNN (probabilistic neural network) models were also explored along with matched case control logistic regression. The stepwise selection procedure yielded the matched logistic regression model indicating the difference between average speeds upstream and downstream as significant. Even though the model provided good interpretation, its classification accuracy over the validation dataset was far inferior to the hybrid MLP/NRBF and PNN models. Hybrid neural network models along with classification tree model (developed to identify the traffic regimes) were able to identify about 60% of the regime 2 rear-end crashes in addition to all regime 1 rear-end crashes with a reasonable number of positive decisions (warnings). It translates into identification of more than ¾ (77%) of all rear-end crashes. Classification models were then developed for the next most frequent type, i.e., lane change related crashes. Based on preliminary analysis, it was concluded that the location specific characteristics, such as presence of ramps, mile-post location, etc. were not significantly associated with these crashes. Average difference between occupancies of adjacent lanes and average speeds upstream and downstream of the crash location were found significant. The significant variables were then subjected as inputs to MLP and NRBF based classifiers. The best models in each category were hybridized by averaging their respective outputs. The hybrid model significantly improved on the crash identification achieved through individual models and 57% of the crashes in the validation dataset could be identified with 30% warnings. Although the hybrid models in this research were developed with corresponding data for rear-end and lane-change related crashes only, it was observed that about 60% of the historical single vehicle crashes (other than rollovers) could also be identified using these models. The majority of the identified single vehicle crashes, according to the crash reports, were caused due to evasive actions by the drivers in order to avoid another vehicle in front or in the other lane. Vehicle rollover crashes were found to be associated with speeding and curvature of the freeway section; the established relationship, however, was not sufficient to identify occurrence of these crashes in real-time. Based on the results from modeling procedure, a framework for parallel real-time application of these two sets of models (rear-end and lane-change) in the form of a system was proposed. To identify rear-end crashes, the data are first subjected to classification tree based rules to identify traffic regimes. If traffic patterns belong to regime 1, a rear-end crash warning is issued for the location. If the patterns are identified to be regime 2, then they are subjected to hybrid MLP/NRBF model employing traffic data from five surrounding traffic stations. If the model identifies the patterns as crash prone then the location may be flagged for rear-end crash, otherwise final check for a regime 2 rear-end crash is applied on the data through the hybrid PNN model. If data from five stations are not available due to intermittent loop failures, the system is provided with the flexibility to switch to models with more tolerant data requirements (i.e., model using traffic data from only one station or three stations). To assess the risk of a lane-change related crash, if all three lanes at the immediate upstream station are functioning, the hybrid of the two of the best individual neural network models (NRBF with three hidden neurons and MLP with four hidden neurons) is applied to the input data. A warning for a lane-change related crash may be issued based on its output. The proposed strategy is demonstrated over a complete day of loop data in a virtual real-time application. It was shown that the system of models may be used to continuously assess and update the risk for rear-end and lane-change related crashes. The system developed in this research should be perceived as the primary component of proactive traffic management strategy. Output of the system along with the knowledge of variables critically associated with specific types of crashes identified in this research can be used to formulate ways for avoiding impending crashes. However, specific crash prevention strategies e.g., variable speed limit and warnings to the commuters demand separate attention and should be addressed through thorough future research.
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Date Issued
2005
Identifier
CFE0000842, ucf:46659
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000842
IMPROVING TRAFFIC SAFETY AND DRIVERS' BEHAVIOR IN REDUCED VISIBILITY CONDITIONS
Title
IMPROVING TRAFFIC SAFETY AND DRIVERS' BEHAVIOR IN REDUCED VISIBILITY CONDITIONS.
Creator
Hassan, Hany, Abde-Aty, Mohamed, University of Central Florida
Abstract / Description
This study is concerned with the safety risk of reduced visibility on roadways. Inclement weather events such as fog/smoke (FS), heavy rain (HR), high winds, etc, do affect every road by impacting pavement conditions, vehicle performance, visibility distance, and drivers' behavior. Moreover, they affect travel demand, traffic safety, and traffic flow characteristics. Visibility in particular is critical to the task of driving and reduction in visibility due FS or other weather events such as...
Show moreThis study is concerned with the safety risk of reduced visibility on roadways. Inclement weather events such as fog/smoke (FS), heavy rain (HR), high winds, etc, do affect every road by impacting pavement conditions, vehicle performance, visibility distance, and drivers' behavior. Moreover, they affect travel demand, traffic safety, and traffic flow characteristics. Visibility in particular is critical to the task of driving and reduction in visibility due FS or other weather events such as HR is a major factor that affects safety and proper traffic operation. A real-time measurement of visibility and understanding drivers' responses, when the visibility falls below certain acceptable level, may be helpful in reducing the chances of visibility-related crashes. In this regard, one way to improve safety under reduced visibility conditions (i.e., reduce the risk of visibility related crashes) is to improve drivers' behavior under such adverse weather conditions. Therefore, one of objectives of this research was to investigate the factors affecting drivers' stated behavior in adverse visibility conditions, and examine whether drivers rely on and follow advisory or warning messages displayed on portable changeable message signs (CMS) and/or variable speed limit (VSL) signs in different visibility, traffic conditions, and on two types of roadways; freeways and two-lane roads. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 566 drivers in Central Florida, USA. Several categorical data analysis techniques such as conditional distribution, odds' ratio, and Chi-Square tests were applied. In addition, two modeling approaches; bivariate and multivariate probit models were estimated. The results revealed that gender, age, road type, visibility condition, and familiarity with VSL signs were the significant factors affecting the likelihood of reducing speed following CMS/VSL instructions in reduced visibility conditions. Other objectives of this survey study were to determine the content of messages that would achieve the best perceived safety and drivers' compliance and to examine the best way to improve safety during these adverse visibility conditions. The results indicated that "Caution-fog ahead-reduce speed" was the best message and using CMS and VSL signs together was the best way to improve safety during such inclement weather situations. In addition, this research aimed to thoroughly examine drivers' responses under low visibility conditions and quantify the impacts and values of various factors found to be related to drivers' compliance and drivers' satisfaction with VSL and CMS instructions in different visibility and traffic conditions. To achieve these goals, Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM) approaches were adopted. The results revealed that drivers' satisfaction with VSL/CMS was the most significant factor that positively affected drivers' compliance with advice or warning messages displayed on VSL/CMS signs under different fog conditions followed by driver factors. Moreover, it was found that roadway type affected drivers' compliance to VSL instructions under medium and heavy fog conditions. Furthermore, drivers' familiarity with VSL signs and driver factors were the significant factors affecting drivers' satisfaction with VSL/CMS advice under reduced visibility conditions. Based on the findings of the survey-based study, several recommendations are suggested as guidelines to improve drivers' behavior in such reduced visibility conditions by enhancing drivers' compliance with VSL/CMS instructions. Underground loop detectors (LDs) are the most common freeway traffic surveillance technologies used for various intelligent transportation system (ITS) applications such as travel time estimation and crash detection. Recently, the emphasis in freeway management has been shifting towards using LDs data to develop real-time crash-risk assessment models. Numerous studies have established statistical links between freeway crash risk and traffic flow characteristics. However, there is a lack of good understanding of the relationship between traffic flow variables (i.e. speed, volume and occupancy) and crashes that occur under reduced visibility (VR crashes). Thus, another objective of this research was to explore the occurrence of reduced visibility related (VR) crashes on freeways using real-time traffic surveillance data collected from loop detectors (LDs) and radar sensors. In addition, it examines the difference between VR crashes to those occurring at clear visibility conditions (CV crashes). To achieve these objectives, Random Forests (RF) and matched case-control logistic regression model were estimated. The results indicated that traffic flow variables leading to VR crashes are slightly different from those variables leading to CV crashes. It was found that, higher occupancy observed about half a mile between the nearest upstream and downstream stations increases the risk for both VR and CV crashes. Moreover, an increase of the average speed observed on the same half a mile increases the probability of VR crash. On the other hand, high speed variation coupled with lower average speed observed on the same half a mile increase the likelihood of CV crashes. Moreover, two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data is advantageous for predicting VR crashes; LDs or AVIs. Thus, this research attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two Expressways (SR 408 and SR 417). Also, it investigates which data is better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression using the historical VR crashes, LDs and AVI data. Regarding models estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 minute prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 minute prior to the crash time, affected the likelihood of VR crash occurrence. Argument concerning which traffic data (LDs or AVI) is better for predicting VR crashes is also provided and discussed.
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Date Issued
2011
Identifier
CFE0003946, ucf:48693
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003946