Estimation and prediction of average vehicle occupancies using traffic accident records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver’s age, and driver’s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Estimation and Prediction of Average Vehicle Occupancies using Traffic Accident Records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver¡¯s age, and driver¡¯s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Effect of delay of first interview and repetition of interview on accuracy and confidence of recall of a flashbulb -type memory

Hearing of the news of the death of Diana, Princess of Wales, in a traffic accident, is taken as an analogue for being a percipient but uninvolved witness to a crime, or a witness to another person's sudden confession to some illegal act. This event (known in the literature as a “reception event”) has previously been hypothesized to cause one to form a special type of memory commonly known as a “flashbulb memory” (FB) (Brown and Kulik, 1977). FB's are hypothesized to be especially resilient against forgetting, highly detailed including peripheral details, clear, and inspiring great confidence in the individual for their accuracy. FB's are dependent for their formation upon surprise, emotional valence, and impact, or consequentiality to the witness of the initiating event. FB's are thought to be enhanced by frequent rehearsal. FB's are very important in the context of criminal investigation and litigation in that investigators and jurors usually place great store in witnesses, regardless of their actual accuracy, who claim to have a clear and complete recollection of an event, and who express this confidently. Therefore, the lives, or at least the freedom, of criminal defendants, and the fortunes of civil litigants hang on the testimony of witnesses professing to have FB's. ^ In this study, which includes a large and diverse sample (N = 305), participants were surveyed within 2–4 days after hearing of the fatal accident, and again at intervals of 2 and 4 weeks, 6, 12, and 18 months. Contrary to the FB hypothesis, I found that participants' FB's degraded over time beginning at least as early as two weeks post event. At about 12 months the memory trace stabilized, resisting further degradation. Repeated interviewing did not have any negative affect upon accuracy, contrary to concerns in the literature. Analysis by correlation and regression indicated no effect or predictive power for participant age, emotionality, confidence, or student status, as related to accuracy of recall; nor was participant confidence in accuracy predicted by emotional impact as hypothesized. Results also indicate that, contrary to the notions of investigators and jurors, witnesses become more inaccurate over time regardless of their confidence in their memories, even for highly emotional events. ^

Visual driver support system for traffic signs

It has been well documented that traffic accidents that can be avoided occur when the motorists miss or ignore traffic signs. With the attention of drivers getting diverted due to distractions like cell phone conversations, missing traffic signs has become more prevalent. Also, poor weather and other unfriendly driving conditions sometimes makes the motorists not to be alert all the time and see every traffic sign on the road. Besides, most cars do not have any form of traffic assistance. Because of heavy traffic and proliferation of traffic signs on the roads, there is a need for a system that assists the driver not to miss a traffic sign to reduce the probability of an accident. Since visual information is critical for driving, processed video signals from cameras have been chosen to assist drivers. These inexpensive cameras can be easily mounted on the automobile. The objective of the present investigation and the traffic system development is to recognize the traffic signs electronically and alert drivers. For the case study and the system development, five important and critical traffic signs have been selected. They are: STOP, NO ENTER, NO RIGHT TURN, NO LEFT TURN, and YIELD. The system was evaluated processing still pictures taken from the public roads, and the recognition results were presented in an analysis table to indicate the correct identifications and the false ones. The system reached the acceptable recognition rate of 80% for all five traffic signs. The processing rate was about three seconds. The capabilities of MATLAB, VLSI design platforms and coding have been used to generate a visual warning to complement the visual driver support system with a Field Programmable Gate Array (FPGA) on a XUP Virtex-II Pro Development System.

Investigation of the causes of serious injury and fatal crashes: An analysis using econometric models

The rate of fatal crashes in Florida has remained significantly higher than the national average for the last several years. The 2003 statistics from the National Highway Traffic Safety Administration (NHTSA), the latest available, show a fatality rate in Florida of 1.71 per 100 million vehicle-miles traveled compared to the national average of 1.48 per 100 million vehicle-miles traveled. The objective of this research is to better understand the driver, environmental, and roadway factors that affect the probability of injury severity in Florida. ^ In this research, the ordered logit model was used to develop six injury severity models; single-vehicle and two-vehicle crashes on urban freeways and urban principal arterials and two-vehicle crashes at urban signalized and unsignalized intersections. The data used in this research included all crashes that occurred on the state highway system for the period from 2001 to 2003 in the Southeast Florida region, which includes the Miami-Dade, Broward and Palm Beach Counties.^ The results of the analysis indicate that the age group and gender of the driver at fault were significant factors of injury severity risk across all models. The greatest risk of severe injury was observed for the age groups 55 to 65 and 66 and older. A positive association between injury severity and the race of the driver at fault was also found. Driver at fault of Hispanic origin was associated with a higher risk of severe injury for both freeway models and for the two-vehicle crash model on arterial roads. A higher risk of more severe injury crash involvement was also found when an African-American was the at fault driver on two-vehicle crashes on freeways. In addition, the arterial class was also found to be positively associated with a higher risk of severe crashes. Six-lane divided arterials exhibited the highest injury severity risk of all arterial classes. The lowest severe injury risk was found for one way roads. Alcohol involvement by the driver at fault was also found to be a significant risk of severe injury for the single-vehicle crash model on freeways. ^

An investigation of location effect on trip production using artificial neural networks

As traffic congestion exuberates and new roadway construction is severely constrained because of limited availability of land, high cost of land acquisition, and communities' opposition to the building of major roads, new solutions have to be sought to either make roadway use more efficient or reduce travel demand. There is a general agreement that travel demand is affected by land use patterns. However, traditional aggregate four-step models, which are the prevailing modeling approach presently, assume that traffic condition will not affect people's decision on whether to make a trip or not when trip generation is estimated. Existing survey data indicate, however, that differences exist in trip rates for different geographic areas. The reasons for such differences have not been carefully studied, and the success of quantifying the influence of land use on travel demand beyond employment, households, and their characteristics has been limited to be useful to the traditional four-step models. There may be a number of reasons, such as that the representation of influence of land use on travel demand is aggregated and is not explicit and that land use variables such as density and mix and accessibility as measured by travel time and congestion have not been adequately considered. This research employs the artificial neural network technique to investigate the potential effects of land use and accessibility on trip productions. Sixty two variables that may potentially influence trip production are studied. These variables include demographic, socioeconomic, land use and accessibility variables. Different architectures of ANN models are tested. Sensitivity analysis of the models shows that land use does have an effect on trip production, so does traffic condition. The ANN models are compared with linear regression models and cross-classification models using the same data. The results show that ANN models are better than the linear regression models and cross-classification models in terms of RMSE. Future work may focus on finding a representation of traffic condition with existing network data and population data which might be available when the variables are needed to in prediction.

A Multi-Criteria GIS-Based Route Selection Tool for Hazardous Material Transport: Consideration of Environmental Consequence, Traffic Congestions and Costs

Hazardous materials are substances that, if not regulated, can pose a threat to human populations and their environmental health, safety or property when transported in commerce. About 1.5 million tons of hazardous material shipments are transported by truck in the US annually, with a steady increase of approximately 5% per year. The objective of this study was to develop a routing tool for hazardous material transport in order to facilitate reduced environmental impacts and less transportation difficulties, yet would also find paths that were still compelling for the shipping carriers as a matter of trucking cost. The study started with identification of inhalation hazard impact zones and explosion protective areas around the location of hypothetical hazardous material releases, considering different parameters (i.e., chemicals characteristics, release quantities, atmospheric condition, etc.). Results showed that depending on the quantity of release, chemical, and atmospheric stability (a function of wind speed, meteorology, sky cover, time and location of accidents, etc.) the consequence of these incidents can differ. The study was extended by selection of other evaluation criteria for further investigation because health risk as an evaluation criterion would not be the only concern in selection of routes. Transportation difficulties (i.e., road blockage and congestion) were incorporated as important factor due to their indirect impact/cost on the users of transportation networks. Trucking costs were also considered as one of the primary criteria in selection of hazardous material paths; otherwise the suggested routes would have not been convincing for the shipping companies. The last but not least criterion was proximity of public places to the routes. The approach evolved from a simple framework to a complicated and efficient GIS-based tool able to investigate transportation networks of any given study area, and capable of generating best routing options for cargos. The suggested tool uses a multi-criteria-decision-making method, which considers the priorities of the decision makers in choosing the cargo routes. Comparison of the routing options based on each criterion and also the overall suitableness of the path in regards to all the criteria (using a multi-criteria-decision-making method) showed that using similar tools as the one proposed by this study can provide decision makers insights in the area of hazardous material transport. This tool shows the probable consequences of considering each path in a very easily understandable way; in the formats of maps and tables, which makes the tradeoffs of costs and risks considerably simpler, as in some cases slightly compromising on trucking cost may drastically decrease the probable health risk and/or traffic difficulties. This will not only be rewarding to the community by making cities safer places to live, but also can be beneficial to shipping companies by allowing them to advertise as environmental friendly conveyors.