Prediction of Fatality Crashes with Multilayer Perceptron of Crash Record Information System Datasets

Despite the effort of the authorities and researchers, there has been no sign of decreasing in the number of fatal crashes annually. To analyze the deadly collisions, researchers have focused on finding which factors affect injury severity, and thus many crash prediction models for it had been developed. Commonly the injury severity is categorized into five different classes. Still, in many studies, minority classes like fatality and incapacitating injury were merged so that the dataset becomes balanced, and the model can provide decent predictions. However, this approach does not help analyze the fatal crashes as they are joined with other types of injury. Therefore, in this study, we proposed a multilayer perceptron model for binary classification of crash fatality. The model was proved to be able to handle heavily imbalanced datasets while providing decent performance. Moreover, a sensitivity analysis was conducted on the input of the model to estimate the importance of crash-related factors

Use of Structural Equation Modelling and Neural Network to Analyse Shared Parking Choice Behaviour

The shared parking mode represents a feasible solution to the persistent problem of parking scarcity in urban areas. This paper aims to examine the shared parking choice behaviours using a combination of structural equation modelling (SEM) and neural network, taking into account both the parking location characteristics and the travellers’ characteristics. Data were collected from a commercial district in Nanjing, China, through an online questionnaire survey covering 11 factors affecting shared parking choice. The method involved two steps: firstly, SEM was applied to examine the influence of these factors on shared parking choice. Following this, the seven factors with the strongest correlation to shared parking choice were used to train a neural network model for shared parking prediction. This SEM-informed model was found to outperform a neural network model trained on all eleven factors across precision, recall, accuracy, F1 and AUC metrics. The research concluded that the selected factors significantly influence shared parking choice, reinforcing the hypothesis regarding the importance of parking location and traveller characteristics. These findings provide valuable insights to support the effective implementation and promotion of shared parking

Perchlorate Removal in Microbial Electrochemical Systems With Iron/Carbon Electrodes

Perchlorate removal was tested in the cathode chamber of microbial electrochemical systems (MESs). Dual-chambers MESs were constructed and operated in batch mode with four kinds of cathode materials including Fe/C particles (Fe/C), zero valent iron particles (ZVI), blank carbon felt (CF), and active carbon (AC). Without external energy supply or perchlorate-reducing microbial pre-enrichment, perchlorate (ClO4-) removal could be achieved in the cathode chambers of MESs at different efficiencies. The highest ClO4- removal rates in these reactors were 18.96 (Fe/C, 100 Ω, 2 days), 15.84 (ZVI, 100 Ω, 2 days), 14.37 (CF, 100 Ω, 3 days), and 19.78 mg/L/day (AC, 100 Ω, 2 days). ClO4- degradation products were mainly Cl− and ClO3-, and the total chlorine in the products was lower than the theoretical input. The non-conservation of the total chlorine may be caused by the adsorption and co-precipitation related to the electrode materials. Coulombs and coulombic efficiency calculation showed that electron provided by MESs was partially responsible for ClO4- reduction, for the Fe/C cathode reactors, about a quarter of electron was provided by MESs

Synthesis of Best Application and Verification Practices for Long-Life Pavement Markings: Final Report

0-7135Proper application of pavement markings plays an important role in enhancing the safety of roadway users. The purpose of this study is to carefully synthesize information on the usage of various types of pavement marking materials (including but not limited to the types of marking materials and quality control approaches, types of specifications, and application rate verification) to identify marking equipment ability and markings payment bases and develop recommendations with regard to different U.S. states and several foreign countries, and to compare them with the Texas Department of Transportation\u2019s (TxDOT) pavement marking practices. To achieve these objectives, a comprehensive online survey was designed in which a wide range of practical information was collected with respect to marking material types and quality control approaches, types of specifications and application rate verification, and equipment and payment bases from field engineers in other states and countries. In addition, manuals and research papers or reports related to pavement marking materials for other state-level departments of transportation (DOTs) and foreign countries were thoroughly reviewed and synthesized. The TxDOT (2004) Pavement Marking Handbook does not incorporate more recently available marking technologies or quality control procedures for the new marking materials. The researchers therefore recommend adoption of best practices from other states and countries to improve the existing handbook. The implementation of the recommendations would help to enhance the efficiency of TxDOT pavement marking practices, including those for marking durability and retro-reflectivity

Intelligent classification, simulation and control of traffic flow

Title of Dissertation: Intelligent Classification, Simulation and Control of Traffic Flow Fengxiang Qiao, Doctoral of Philosophy, 2000 Dissertation directed by: Asso. Prof. H. Yang Department of Civil Engineering The Hong Kong University of Science and Technology Most of physical traffic models, which can explain many of the traffic phenomena and are widely used in the analysis, management, control and guidance of traffic flow, are normally in well-posted mathematical forms. However due to the complexity of traffic properties, physical models in some cases can not really match the depicted actual phenomena even though certain correcting factors are introduced. Hence it is hoped to develop intelligent models that can auto-adapt, or self-calibrate from the input-output data in the applications. Although some pioneer works on these intelligent data dependant models have been reported recently, the researches on these kinds of models are far fewer than required. This dissertation aims to apply a series of intelligent and robust approaches to some of the problems in the classification, simulation and control of traffic flow. At first, the efficient, yet practical, method for the classification of traffic flow states on highways was developed. The method sampled actual flow data for each possible case, recognized their different characteristics, and then sorted them into various clusters using neural network pattern recognition techniques. A small-scale test with actual data was conducted, and the method was found to be potentially applicable in practice. Then, a simulation approach was proposed to analyze the occurrence of traffic conflicts at unsignalized intersections. The simulation model can provide some useful statistics on traffic situations at unsignalized intersections, which are also useful for safety assessment and examination of the necessity for traffic control devices such as road signs or traffic signal controls. After that, a neural network based system identification (NNSI) approach was used to establish an auto-adaptive model for simulating and forecasting the dispersion of traffic flow on road segments. The structure and linking weights of the neural network based model could be on-line calibrated, and thus it could be applicable for operations under varying traffic environments. Simulation and field validation results provide strong evidence of the good performance for the proposed neural network based system identification approach. Next, the fuzzy logic based delay and performance index estimation systems and the corresponding intersection timing techniques were proposed, together with simulation study and field studies. The fuzzy logic based delay estimation approach, which can combine the complex technical and non-technical factors, can be adaptively suitable to the changing environment and easily implemented. The fuzzy performance index system can give a general index for the optimization of a signal timing plan at an intersection. The rule base of the fuzzy performance index system came from the psychology survey at the intersection concerned. This approach can be used not only in isolated intersection timing but also in arterial signal timing, network signal timing, traffic guidance systems etc. Wide applications can be expected in the field of traffic control engineering. Finally, the robust control theory was introduced into the traditional freeway ramp metering system. Using the data dependent system approach, the freeway plant can be identified the on-line system operation with no predefined knowledge on the flow-density relationship, while the sub-optimal robust controller is constructed to reduce some kinds of uncertainties possibly existing in the control system. The simulated results show that the controlled freeway density can be maintained around the desired target within a small error band converging quickly. Widely promising applications can be found in freeway management systems

Intersection delay estimation and signal timing using fuzzy system

Conventional models estimating vehicle delay are all established only based on technical factors with little considering non-technical factors, which can not be depicted in engineering models directly. Even for technological factors, conventional models consider them only by introducing some adjusting factors, which varies from place to place and are difficult to be calibrated. In this papaer, a fuzzy table look-up system for estimating time delay is built up based on rule base containing technical and non-technical information from actual surveyed data sets. By changing timing parmeters (cycle, green time, and etc.), optimal timing strategy with minimum total delay can be easily obtained. An example is given to illustrate the proposed approach. This approach can be used not only in isolated intersection timing but also in arterial signal timing, network signal timing, traffic guidance system and etc. Wide applications can be expected in the field of traffic control engineering

Impacts of pavement types on in-vehicle noise and human health

Noise is a major source of pollution that can affect the human physiology and living environment. According to the World Health Organization (WHO), an exposure for longer than 24 hours to noise levels above 70 dB(A) may damage human hearing sensitivity, induce adverse health effects, and cause anxiety to residents nearby roadways. Pavement type with different roughness is one of the associated sources that may contribute to in-vehicle noise. Most previous studies have focused on the impact of pavement type on the surrounding acoustic environment of roadways, and given little attention to in-vehicle noise levels. This paper explores the impacts of different pavement types on in-vehicle noise levels and the associated adverse health effects. An old concrete pavement and a pavement with a thin asphalt overlay were chosen as the test beds. The in-vehicle noise caused by the asphalt and concrete pavements were measured, as well as the drivers’ corresponding heart rates and reported riding comfort. Results show that the overall in-vehicle sound levels are higher than 70 dB(A) even at midnight. The newly overlaid asphalt pavement reduced in-vehicle noise at a driving speed of 96.5 km/hr by approximately 6 dB(A). Further, on the concrete pavement with higher roughness, driver heart rates were significantly higher than on the asphalt pavement. Drivers reported feeling more comfortable when driving on asphalt than on concrete pavement. Further tests on more drivers with different demographic characteristics, along highways with complicated configurations, and an examination of more factors contributing to in-vehicle noise are recommended, in addition to measuring additional physical symptoms of both drivers and passengers.Implications: While there have been many previous noise-related studies, few have addressed in-vehicle noise. Most studies have focused on the noise that residents have complained about, such as neighborhood traffic noise. As yet, there have been no complaints by drivers that their own in-vehicle noise is too loud. Nevertheless, it is a fact that in-vehicle noise can also result in adverse health effects if it exceeds 85 dB(A). Results of this study show that in-vehicle noise was strongly associated with pavement type and roughness; also, driver heart rate patterns presented statistically significant differences on different types of pavement with different roughness

Socio-demographic impacts on lane-changing response time and distance in work zone with Drivers' Smart Advisory System

Lane-changing behavior is an important component of traffic simulation. A lane-changing action is normally confined to a decision-making process of the task, and the action itself is sometimes assumed as an instantaneous event. Besides, the lane-changing behavior is based mostly on observable positions and speeds of other vehicles, rather than on vehicles' intentions. In practice, changing one lane requires about 5–6 s to complete. Existing lane-changing models do not comprehensively consider drivers' response to work zone lane-changing signs (or other related messages, if any). Furthermore, drivers' socio-demographics are normally not taken into account. With regard to this, fuzzy logic-based lane-changing models that consider drivers' socio-demographics were developed to improve the realism of lane-changing maneuvers in work zones. Drivers' Smart Advisory System (DSAS) messages were provided as one of the scenarios. Drivers' responses, including reactions to work zone signs and DSAS messages, and actions to change lane, were investigated. Drivers' socio-demographic factors were primary independent variables, while Lane-Changing Response Time (LCRT) and Distance (LCRD) were defined as output variables. The model validation process yielded acceptable error ranges. To illustrate how these models can be used in traffic simulation, the LCRT and LCRD in work zones were estimated for five geo-locations with different socio-demographic specifications. Results show that the DSAS is able to instruct all drivers to prepare and change lanes earlier, thereby shortening the duration of changing lanes. Educational background and age are essential variables, whereas the impacts of gender on the output variables are indistinctive

Vehicle emission implications of drivers’ smart advisory system for traffic operations in work zones

ABSTRACT: Wireless communication systems have been broadly applied in various complicated traffic operations to improve mobility and safety on roads, which may raise a concern about the implication of the new technology on vehicle emissions. This paper explores how the wireless communication systems improve drivers’ driving behaviors and its contributions to the emission reduction, in terms of Operating Mode (OpMode) IDs distribution used in emission estimation. A simulated work zone with completed traffic operation was selected as a test bed. Sixty subjects were recruited for the tests, whose demographic distribution was based on the Census data in Houston, Texas. A scene of a pedestrian’s crossing in the work zone was designed for the driving test. Meanwhile, a wireless communication system called Drivers Smart Advisory System (DSAS) was proposed and introduced in the driving simulation, which provided drivers with warning messages in the work zone. Two scenarios were designed for a leading vehicle as well as for a following vehicle driving through the work zone, which included a base test without any wireless communication systems, and a driving test with the trigger of the DSAS. Subjects’ driving behaviors in the simulation were recorded to evaluate safety and estimate the vehicle emission using the Environmental Protection Agency (EPA) released emission model MOVES. The correlation between drivers’ driving behavior and the distribution of the OpMode ID during each scenario was investigated. Results show that the DSAS was able to induce drivers to accelerate smoothly, keep longer headway distance and stop earlier for a hazardous situation in the work zone, which driving behaviors result in statistically significant reduction in vehicle emissions for almost all studied air pollutants (p-values range from 4.10E-51 to 2.18E-03). The emission reduction was achieved by the switching the distribution of the OpMode IDs from higher emission zones to lower emission zones. Implications: Transportation section is a significant source of greenhouse gas emissions. Many studies demonstrate that the wireless communication system dedicated for safety and mobility issues may contribute to the induction in vehicle emissions through changing driving behaviors. An insight into the correlation between the driving behaviors and the distribution of Operating Mode (OpMode) IDs is essential to enhance the emission reduction. The result of this study shows that with a Drivers Smart Advisory System (DSAS) drivers accelerated smoothly and stopped earlier for a hazardous situation, which induce the switch of the OpMode IDs from high emission zones to lower emission zones

Implications of advanced warning messages on eliminating sun glare disturbances at signalized intersections

Due to sun glare disturbances, drivers encounter fatal threats on roadways, particularly at signalized intersections. Many studies have attempted to develop applicable solutions, such as avoiding sun positions, applying road geometric re-directions, and wearing anti-glare glasses. None of these strategies have fully solved the problem. As one of the “Connected Vehicle” practices proposed by the U.S. Department of Transportation, advanced warning messages (AWMs) are capable of providing wireless information about traffic controls. AWM acts as a supplement to conventional signs and signals, which can be blocked by obstacles or natural disturbances, such as sun glare. The drivers' smart advisory system (DSAS) can provide drivers with AWM. Using a driving simulator this research explores the effects of DSAS messages on driving behaviors under sun glare disturbance. Statistical analyses were applied to assess (1) the negative impacts of sun glare, (2) the compensation of the DSAS AWM to sun glare effects, and (3) the improvement in driving performance due to DSAS AWM. Four performance indexes were measured, including (1) half kinetic energy speed, (2) mean approach speed, (3) brake response time, and (4) braking distance. The effects of the socio-demographic factors, such as gender, age, educational background, and driving experience were also studied. The analytical results illustrate that the DSAS can compensate for reduced visibility due to sun glare and improve driving performance to a normal visual situation, particularly for left turn and through movement