The primary objective of this article is to contribute to the debate on the relationship between average speeds, speed variations, and accident rates. This is achieved by the use of two advanced statistical models: (1) a nonspatial random-effects negative binomial model and (2) a spatial Poisson-lognormal model using a full hierarchical Bayesian model to explore the relationship. Disaggregated segment-based traffic, road geometry, and accident data from 266 road segments including 13 different motorways (including the M25 motorway) and 17 different trunk A-class roads around London from 2003 to 2007 are used in the analysis. GIS tools are used to achieve the appropriate data and to derive the weight matrix among neighboring segments that is necessary for the spatial model. The results suggest that average speeds are not associated with accident rates when controlling for other factors affecting accidents such as traffic volume, road geometry (e.g., grade and curvature), and number of lanes. However, speed variation is found to be statistically and positively associated with accident rates. A 1% increase in speed variation is associated with a 0.3% increase in accident rates, ceteris paribus. The results for all other factors are found to be consistent with existing studies. Policy implications of the findings are then discussed.
Parking search creates environmental and economic externalities through heightened vehicle pollutant emissions, congestion-related time delays and safety hazards arising from drivers performing on-street parking manoeuvres. Current modelling techniques applied to parking search time have not utilised a more comprehensive analysis in which hierarchically structured data on multiple levels could be addressed. The aim of this paper is to identify factors influencing parking search time using multilevel mixed-effects linear regression models. A revealed-preference on-street parking survey (N=1,003) was undertaken in four cities in the East Midlands region of the UK to obtain individual driver-level socioeconomic and other parking related factors that may influence parking search time. Statistically significant variables for each of the cities were identified by employing separate linear regression models. A multilevel mixed-effects model in which drivers are nested within streets was then applied to the pooled dataset. Significant differences in the set of statistically significant variables and coefficient values of influencing factors between the two modelling approaches were obtained. A large value of intra-class coefficient (ICC) indicates that the use of a multilevel model in identifying the factors affecting on-street parking search time is more appropriate. Important factors were identified as: arrival time at parking place; trip length from origin to parking; parking habits; driver stress; walking distance from parking to destination; parking tariff; income; and weather. Interpretation of these variables has been presented.
Cooperative, Connected and Automated Mobility (CCAM) enabled by Connected and Autonomous Vehicles (CAVs) has potential to change future transport systems. The findings from previous studies suggest that these technologies will improve traffic flow, reduce travel time and delays. Furthermore, these CAVs will be safer compared to existing vehicles. As these vehicles may have the ability to travel at a higher speed and with shorter headways, it has been argued that infrastructure-based measures are required to optimise traffic flow and road user comfort. One of these measures is the use of a dedicated lane for CAVs on urban highways and arterials and constitutes the focus of this research. As the potential impact on safety is unclear, the present study aims to evaluate the safety impacts of dedicated lanes for CAVs. A calibrated and validated microsimulation model developed in AIMSUN was used to simulate and produce safety results. These results were analysed with the help of the Surrogate Safety Assessment Model (SSAM). The model includes human-driven vehicles (HDVs), 1st generation and 2nd generation autonomous vehicles (AVs) with different sets of parameters leading to different movement behaviour. The model uses a variety of cases in which a dedicated lane is provided at different type of lanes (inner and outer) of highways to understand the safety effects. The model also tries to understand the minimum required market penetration rate (MPR) of CAVs for a better movement of traffic on dedicated lanes. It was observed in the models that although at low penetration rates of CAVs (around 20%) dedicated lanes might not be advantageous, a reduction of 53% to 58% in traffic conflicts is achieved with the introduction of dedicated lanes in high CAV MPRs. In addition, traffic crashes estimated from traffic conflicts are reduced up to 48% with the CAVs. The simulation results revealed that with dedicated lane, the combination of 40-40-20 (i.e., 40% human-driven - 40% 1st generation AVs- 20% 2nd generation AVs) could be the optimum MPR for CAVs to achieve the best safety benefits. The findings in this study provide useful insight into the safety impacts of dedicated lanes for CAVs and could be used to develop a policy support tool for local authorities and practitioners.
Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson) for modeling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilizes more detailed individual accident level data and it is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.
The aging of populations has implications for trip-making behavior and the demand for special transport services. The London Area Travel Survey 2001 is analyzed to establish the trip-making characteristics of elderly and disabled people. Ordinal probit models are fitted for all trips and for trips by four purposes (work, shopping, personal business, and recreational), with daily trip frequency as the latent variable. A log-linear model is used to analyze trip length. A distinction must be made between young disabled, younger elderly, and older elderly people. Retired people initially tend to make more trips, but as they become older and disabilities intervene, trip making tails off. Household structure, income, car ownership, possession of a driver's license, difficulty walking, and other disabilities are found to affect trip frequency and length to a greater or lesser extent.
The rise in private car use in recent years has led to a dispersal of activity centres and an associated challenge in providing public transport to meet the needs of a large section of the population. Governments now see public transport as important in meeting an ever expanding range of public policy goals, but costs in providing bus services are increasing amidst economic uncertainty. There is a need for new cost-effective modes of transport that can operate effectively in areas where demand levels are lower and more dispersed. Such modes include Demand Responsive Transport (DRT), an intermediate form of public transport, encompassing a whole range of service delivery options. However, unlike for buses, less research has been carried out to determine how, why, when and where DRT services will function effectively. The primary objective of this paper is to examine the potential use of DRT services. This included the development of a forecasting framework for a new DRT service on a transport corridor. Forecasts for these services were generated from a stated preference based survey of over 400 respondents in urban (Rochdale, Manchester) and rural (Melton Mowbray, Leicestershire) areas in the UK. DRT modal share forecasts have been generated that show a greater DRT propensity for current bus users than those with access to a car. Model estimations reinforce the importance of price on modal choice, including motoring costs and the fare levels for DRT and bus services
Map-matching algorithms that utilise road segment connectivity along with other data (i.e. position, speed and heading) in the process of map-matching are normally suitable for high frequency (1 Hz or higher) positioning data from GPS. While applying such map-matching algorithms to low frequency data (such as data from a fleet of private cars, buses or light duty vehicles or smartphones), the performance of these algorithms reduces to in the region of 70% in terms of correct link identification, especially in urban and sub-urban road networks. This level of performance may be insufficient for some real-time Intelligent Transport System (ITS) applications and services such as estimating link travel time and speed from low frequency GPS data. Therefore, this paper develops a new weight-based shortest path and vehicle trajectory aided map-matching (stMM) algorithm that enhances the map-matching of low frequency positioning data on a road map. The well-known A∗ search algorithm is employed to derive the shortest path between two points while taking into account both link connectivity and turn restrictions at junctions. In the developed stMM algorithm, two additional weights related to the shortest path and vehicle trajectory are considered: one shortest path-based weight is related to the distance along the shortest path and the distance along the vehicle trajectory, while the other is associated with the heading difference of the vehicle trajectory. The developed stMM algorithm is tested using a series of real-world datasets of varying frequencies (i.e. 1 s, 5 s, 30 s, 60 s sampling intervals). A high-accuracy integrated navigation system (a high-grade inertial navigation system and a carrier-phase GPS receiver) is used to measure the accuracy of the developed algorithm. The results suggest that the algorithm identifies 98.9% of the links correctly for every 30 s GPS data. Omitting the information from the shortest path and vehicle trajectory, the accuracy of the algorithm reduces to about 73% in terms of correct link identification. The algorithm can process on average 50 positioning fixes per second making it suitable for real-time ITS applications and services.
A map-matching algorithm employs data from GPS, a GIS-based road map and other sensors to first identify the correct link on which a vehicle travels and then to determine the physical location of the vehicle on the link. Due to the uncertainties associated with the raw measurements from GPS/other sensors, the road map and the related methods, it is necessary to monitor the integrity of map-matching results, especially for safety and mission-critical land vehicle navigation. Current integrity methods for map-matching are inadequate and unreliable as they fail to satisfy the integrity requirement due mainly to incorrect treatment of all the related uncertainties simultaneously. The aim of this paper is therefore to develop a new tightly-coupled integrity monitoring method for map-matching by properly treating the uncertainties from all sources concurrently. In this method, the raw measurements from GPS, low-cost Dead-Reckoning (DR) sensors and Digital Elevation Model (DEM) are first integrated using an Extended Kalman Filter (EKF) to continuously obtain better position fixes. A weight-based topological map-matching process is then developed to map-match position fixes on to the road map. The accuracy of the map-matching process is enhanced by employing a range of network features such as grade separation, traffic flow directions and the geometry of a road link. The Receiver Autonomous Integrity Monitoring (RAIM) technique, which has been successfully applied to monitor the integrity of aircraft navigation, is modified and enhanced so as to apply it to monitor the quality of map-matching. In the enhanced RAIM method, two modifications are made: (1) a variable false alarm rate (as opposed to a constant false alarm rate) is considered to improve the fault detection performance in selecting the links, especially near junctions. (2) a sigma inflation for a non-Gaussian distribution of measurement noises is applied for the purpose of satisfying the integrity risk requirement. The implementation and validation of the enhanced RAIM method is accomplished by utilising the required navigation performance (RNP) parameters (in terms of accuracy, integrity and availability) of safety and mission-critical intelligent transport systems. The required data were collected from Nottingham and central London. In terms of map-matching, the results suggest that the developed map-matching method is capable of identifying at least 97.7% of the links correctly in the case of frequent GPS outages. In terms of integrity, the enhanced RAIM method provides better the fault detection performance relative to the traditional RAIM.
In recognition of the importance of protecting life and property, this chapter focuses on two distinct but intrinsically interrelated fields: aviation safety and aviation security. The first part of the chapter examines aviation safety. It introduces the concept of safety, details the principal causes of aircraft accidents and describes the causation models that can be used to reduce the likelihood of future occurrences. As aircraft and airports represent a target for terrorist attack, the second part of the chapter focuses on aviation security. It examines why air transport is targeted, discusses the evolution of aviation security regimes and details the security interventions that have been developed to try and protect the industry from acts of unlawful interference.
Highway works are highly inconvenient and disruptive for society. Accordingly, four highway policy interventions were investigated in Derby, UK, for potential corresponding reductions in highway works durations. Time series analysis was used to test the durational impacts on works led by Highway Authorities (HAs) and utility industries. The modelling results demonstrated that a highway works management permit scheme (chargeable) reduced utility works durations by 5·4% (727 work days annually). Conversely, three conflated interventions – namely, the permit scheme (cost-free to HAs), JCB Pothole Master deployment and construction direct labour organisation – did not make any statistically significant difference to HA works durations; however, introducing an automated works order management system (Woms) reduced HA works duration by 34% (6519 work days annually). The key finding of this study is that chargeable permit schemes can create the impetus for change, as demonstrated by the utility industry. Furthermore, the Woms revealed that back-office efficiency can lead to on-site efficiency in works execution.
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