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Driver behaviour has become an important aspect of transport research and over the years a considerable number of car following models have been developed. However, many of these models do not accurately simulate actual driving behaviour due to a lack of suitable qualitative and quantitative data. Moreover, the inclusion of socioeconomic variables in the existing models to ascertain the effect on car following behaviour is lacking. This research underlines the need to further investigate driving behaviour and car following models and to develop techniques to provide a better understanding of driver-vehicle interactions during car following. It investigates data collection techniques and develop better techniques to enhance and improve the collection of microscopic driver behaviour and traffic flow data. This study developed a novel data collection technique which involved instrumenting a private vehicle with front and rear advanced radar sensors, both forward and rear facing video-audio recorders connected to GPS based time series speed and distance measurement devices, an in-vehicle computer logging vehicle speed and a CAN monitoring interface user program to provide real time monitoring and display of data. This system has been utilised to collect a more enhanced and reliable microscopic driver behaviour data in three consecutive vehicles movements which represents an improvement from previously used systems. Three different versions of the GHR car following model were produced for: car following car, truck following car and car following truck. Further analysis of the GHR model showed that in the case of car following car, car drivers responses to the lead car are more obviously stronger than in the case of truck following a car. A distance-based car following model and distance-based two-leader car following model that predict the safe following distance of following vehicles were developed to provide a better understanding of driver behaviour. An extension of these models to include gender, corridor (road) type and vehicle occupancy showed evidence of statistical significance of these variables on driver behaviour. A bus following model that predicts the “following distance” also has been calibrated to describe the interactions between a bus and a car within urban-rural driving conditions. In addition, data analysis showed that drivers were inconsistent with their driving behaviour and that there was variability in driving behaviour across the drivers observed in keeping a safe or desired following distance. This study provides a platform for a number of future research agendas including data collection techniques for collection of driver behaviour data; evaluation of different ITS technologies; impact assessment of ACC on driver safety and improvement of traffic microscopic simulation tools in order to strengthen their ability to simulate realistic transport problems for efficient and effective transportation systems.

Traffic congestion today is a major problem in almost all of the metropolitan areas of the world. An increasing level of congestion results in negative impacts on the urban environment. These include environmental pollution, energy problems and traffic accidents. The analysis of these problems and the predictions of the impacts of any transport policies that could be devised to deal with them are very critical to their success. Traffic problems are almost the same in most modern cities either in developed countries or less economically developed countries. The driving cycle for a vehicle is the representation of a speed–time sequenced profile, which is developed for a specific area or city. It is an important requirement in the evaluation of the driver's behaviour and the performance of vehicles for a number of applications, mainly in the area of environmental studies. For example, fuel consumption and emissions' predictions need information input on the characteristics of driving patterns of traffic. The applications of driving cycle analysis can be extended however, to many more other areas. The motivation for this research is to investigate the detailed impacts of travel demand management (TDM) measures, that are already in application. This is to improve the network performance, using driving cycle analysis. It is important to explicitly assess these measures using a micro-level detailed approach in order to comprehend overall results in terms of emissions and network performance. These understandings will benefit government agencies and policy makers in their planning and appraisals. It will also benefit public transport providers to improve their service in attracting and retaining their customers. The developments of the real world driving cycles in Edinburgh and Abu Dhabi have been presented in this research. The analysis of real world data, which has been obtained from monitoring traffic conditions in both cities using the GPS tracking of traffic, is presented. This data was collected from trips which have been carried out on a number of traffic corridors in both cities. The assessment of various parameters of traffic (i.e. speed, time percentage spent on acceleration, deceleration, idling, cruising and cycle duration) and their statistical validity, produced a real world driving cycle for the buses as well as the private cars. Two TDM measures have been considered; bus lanes and traffic calming measures. At each corridor, a handheld GPS device was used to record speed, acceleration, deceleration and distances driven. This data enabled the analysis of driving cycles for the buses and for the private cars. The driving cycle analysis and investigations have further been investigated using regression analysis techniques. The results suggest that the approach shows potential but further research is needed with more data available. The results suggest that the driving cycle analysis approach would be very useful to have a better understanding of driving behaviour and also the detailed impacts of the transport policies on traffic. In terms of bus lanes and traffic calming measures, the results show some positive impacts of these policies, while there are evidences of some negative impacts as well. These findings would be very valuable for the policy decision makers. It is recommended from this research that the driving cycle analysis could be utilised effectively in the assessment of TDM measures. Further investigations and analysis of driving cycle is urgently recommended in a number of research directions. Combined GIS and GPS data could also enhance the development in this research.

The review of available literature related to pedestrian accidents indicates that the occurrence of pedestrian accidents is influenced by a diverse range of factors. However, few empirical studies have documented the effects of distance of pedestrian accidents from pedestrian crossing area or junction. The few studies which investigated the impact of the distance from the crossing line on pedestrian accidents, suggest that the longer the distance from road crossing facilities, the higher the likelihood of a pedestrian accident. With respect to the influence of the type of pedestrian crossing on the incidence of pedestrian accidents, a substantial body of literature has found that the types of pedestrian crossing indeed affect the frequency of pedestrian collisions. Additionally, all the available studies reviewed indicated the positive impact of signalised crossings on the reduction of pedestrian collision risk. Data from STATS19 show that pedestrian severity rates are higher over the pedestrian crossing points or within 50 meters of pedestrian crossing facilities than those away from it. This is contrary to the expectations that accidents should be least over these crossing facilities. This study investigates in more detail the factors that affect accident occurrence at signalised pedestrian junction and pelican or similar type of crossing facilities in the Scotland area. The main objective of this current research has been to investigate those factors most commonly associated with pedestrian injury severity at a pedestrian crossing or within 50m of one. Accident data of 14 years (from 1993 until 2006) in selected sites show that 942 pedestrian accidents occurred on or within 50m of a signalised pedestrian crossing area. Grid references of accident locations as well as locations of pedestrian crossings were obtained from the STATS 19 database and the local city council. The data was used to identify the locations of accidents relative to the location of pedestrian crossing facilities. In terms of severity of injuries models, results suggest that pedestrians from the older group received more severe injuries, compared with those from younger groups. Again, this finding underlines the importance of regulations and subsequent enforcement of traffic laws that protect and promote the safety of older pedestrians. The models also showed an association between the severity of injury and the type of pedestrian crossing. Since more KSI accidents have been associated with pelican crossings, there may be a need to undertake raising awareness and education for pedestrians to improve pedestrian safety. In terms of ROW models; it was shown that turning manoeuvres were more likely to violate pedestrian's ROW and result in accidents than other types of manoeuvres. Moreover, the model showed that heavy-goods vehicles and cars are associated with pedestrian's ROW, as compared to other types of vehicles. The various issues related to accidents resulting from pedestrian right-of-way can be effectively resolved by rationalisation of pedestrian crossing types; and provision of education with regards to the rules and responsibilities of both pedestrians and drivers at all available crossings. The models developed to profile pedestrian accidents in Edinburgh suggest that the highest number of pedestrian accidents occurred at pedestrian crossing lines; and that the number of pedestrian accidents decreased when moving away from pedestrian crossing lines or within 50 metres of pedestrian crossing lines. These have serious implications in terms of requiring improvements to pedestrian crossing facilities that can then ensure better pedestrian visibility and provide the public with more protection from moving vehicles. Moreover, another implication of this finding is that more regulatory instruments must be revalidated and further developed, since there are no laws to prevent pedestrians from crossing the road at certain points. The only laws being enforced in the UK are those relating to the prohibition of walking on motorways or slip roads but not regarding loitering on pedestrian crossings. Therefore, the guidelines specified in the Highway Code to deal with pedestrian behaviour while crossing the road have to be revisited and further developed. The results show that accidents rates decrease as distance increase from the pedestrian crossing facilities. The most risky locations are those at the pedestrian crossings or within 10 meters and the distance from 10 to 30 meters before the pedestrian crossing facilities. Analysis of pedestrian accidents rates and severities for each of pelican and signalised crossings were discussed. An investigation of right-of-way violations associated with pedestrian accidents at pedestrian crossing areas or within 50 metres of the same was carried out. Modelling accidents rates and severities at these pedestrian crossings is also presented in this thesis. Multinomial logit, ordinal and probit logit and binary logit modelling are used to analyse the results.

Trip generation is the first stage of the conventional 'four-stage' transport model. The aim of this stage is to predict total number of trips generated to and from each zone. The two most common techniques for trip generation are linear regression (the dependent vaziable is alinear-in-parameter function of a number of explanatory variables) and category analysis including multiple classification analysis (based on estimating number of trip generations as a function of household attributes). Both techniques of trip generation rely on the availability of a large socio-economic, mainly revealed preference data set. They also have technical limitations such as the assumption of linearity which might result in unreasonable predictions of trip generation. Any deficiency or inaccuracy in the estimation at this stage will be carried over and will have implications on subsequent stages. The other stages of the 'four-stage' model employ other techniques including logistic analysis which broadens the scope of the analysis. Logistic regression analysis has been used to model travel choices such as mode, route and departure time but not trip generation. There has not been much research to investigate the appropriateness of using this technique to model generation. The main reason for this is that logistic regression predicts probabilities rather than the total number of trips. In order to be able to model trip generation using logistic regression, the number of trips frequency) can be treated as a set of mutually exclusive categorical variables; therefore the built-in upper and lower limits are incorporated. Therefore, it is not possible to predict a negative number of trips and the estimates of the model will show the underlying probabilities for the actual number of This will also provide a behavioural framework that directly links the number of trips to utility-based consumer and decision-making theory. Logistic regression can be used to model trip generation as binary, multinomial or nested logit frameworks. An added advantage of using this approach is the ability to predict the frequency and number of trips made by each individual. The aim of this research therefore, is to investigate possible methodologies to improve performance of trip generation modelling. In order to achieve this aim firstly, this research investigates the appropriateness of logistic regression to model trip generation and device a methodology for it. The analysis and comparisons of the results with results from conventional models are examined. Exploring the use of stated preference data to calibrate trip generation models is also studied here. Finally, transport policy measures and enhanced transport accessibility functions have been investigated in generation models.

The level of ownership and use of motorcycling has increased rapidly in Edinburgh and the UK in the last ten years. In this study, motorcycle driving cycles (rural and urban) were developed for Edinburgh (Edinburgh Motorcycle Driving cycle-EMDC). The analysis of EMDC demonstrates that motorcycles‘ driving behaviour differs between urban and rural areas. EMDC shows a typical transient nature of speed, acceleration and deceleration, which is also different from regulatory driving cycles (Economic Commission for Europe-ECE and World Motorcycle Test Cycle-WMTC) and examples from Asia (Taiwan, Bangkok and China). This research underlines the need for detailed investigations of driving cycles in any local condition. It is not generally feasible for a driving cycle developed in one area to be applicable in another area, even with some similar characteristics. Emission factors were also estimated using onboard, laboratory and micro simulation measurements along the test corridor (Air Quality Management Area-AQMA). Laboratory measurements were carried out by applying a number of standard driving cycles (ECE and WMTC) and the derived EMDCs. Results show that the emission factors (EFs) calculated in the laboratory for carbon monoxide (CO) and Hydrocarbons (HC) are higher for the urban EMDC cycle compared to the standard regulatory factors than they are for the rural (except Nitrogen Oxide-NOx). Laboratory emission factors for CO and HC for the urban EMDC were found to be higher than the micro-simulation and onboard methods. EFs obtained from micro-simulation and onboard emissions using the National Atmospheric Emission Inventory (NAEI) emission coefficients were not very different with the exception of NOx, which were relatively higher than those of EMDC. Micro simulation models were mainly developed for private cars and therefore special care should be taken when using them for modelling other conditions (e.g. motorcycles driving characteristics). This study illustrates the extent to which micro-simulation may be utilised to accurately model emissions and discusses the refinements required to model motorcycle motion (hence emission) accurately in micro simulation. The study provides a platform for a large number of potential future applications for the evaluation of emissions and for developing various policy scenarios of pollution reduction and reducing health impacts at local levels.

Motorcyclists that have no protective structures while motorcycling as other occupants of automobiles do can be particularly vulnerable to accident injuries (i.e., motorcycles are not as crashworthy as automobiles). Motorcyclists' susceptibility to accident injuries in nature may act synergistically with the complexity of conflicting manoeuvres between motorcycles and other motor vehicles to increase their injury severities in accidents that take place at junctions (e.g., T-junction or crossroad). Previous studies have applied crash prediction models to investigate influential factors on the occurrences of different crash configurations among automobiles but statistical models of motorcyclist injury severity resulting from different motorcycle-car crash configurations have rarely been developed. This current research attempts to develop the appropriate statistical models of motorcyclist injury severity by various crash configurations conditioned on crash occurrence at T-junctions in the UK. T-junctions are selected in this study because such junctions represent the single greatest danger to motorcyclists - for junction-type accidents, the statistics from the UK Stats19 accident injury database over the years 1991 and 2004 suggested that T-junctions were ranked the highest in terms of injury severity (Le., accidents at T-junctions resulted in approximately 65% of all casualties that sustained fatal or serious injuries) and accident occurrence (i.e., accidents at Tjunctions accounted for 62% of all motorcyclist casualties). This may be in part because there is a comparatively large number ofT-junctions in the UK. Although the author was unable to take into account the exposure factor due to the lack of such data (Le., the total number of T-junctions, and the number of motorcycles travelling on these locations), it remains true that more severe accidents happen at T-junctions than any other type of junction. In this present study, motorcycle-car accidents at Tjunctions were classified into several crash configurations based on two methods that have been widely used in literature. The first method is based on the conflicts that arise from the pre-crash manoeuvres of the motorcycle and car. The second method is on the basis of first points of impact of the motorcycle and car. The crash configurations that are classified in this current study based on the mixture of these two methods include (a) accidents involving gap acceptance (i.e., approach-turn crash and angle crash), (b) head-on crash, and (c) same-direction crash (i.e., sideswipe crash and rear-end crash). Since injury severity levels in traffic accidents are typically progressive (ranging from no injury to fatal/death), the ordered response models have come into fairly wide use as a framework for analysing such responses. Using the accident data extracted from the Stats19 accident injury database over 14-year period (1991~2004), the ordered probit (OP) model of motorcyclist injury severity were estimated because the dependent variable (i.e., no injury, slight injury, KSI: killed or seriously injured) is intrinsically discrete and ordinal. A set of the independent variables were included as the predictor variables, including rider/motorist attributes, vehicle factors, weather/temporal factors, roadway/geometric characteristics, and crash factors. The current research firstly estimated the aggregate OP model of motorcyclist injury severity by motorcycle-car accidents in whole. Additional disaggregate models of motorcyclist injury severity by various crash configurations were subsequently conducted. It appears in this current research that while the aggregate model by motorcycle-car accidents in whole is useful to uncover a general overview of the factors that were associated with the increased motorcyclist injury severity, the dis aggregate models by various crash configurations provide valuable insights (that may not be uncovered by an aggregate crash model) that motorcyclist injury severity in different crash configurations are associated with different pre-crash conditions. For example, the preliminary analysis by conducting descriptive analysis reveals that the deadliest crash manner in approach-turn crashes and angle crashes was a collision in which a right-turn car collided with an approaching motorcycle. Such crash patterns that occurred at stop-/give-way controlled junctions appear to exacerbate motorcyclist injury severity. The disaggregate models by the deadliest crash manners in approachturn crashes and angle crashes suggest that injuries tended to be more severe in crashes where a right-turn motorist was identified to fail to yield to an approaching motorcyclist. Other disaggregate crash models also identified important determinants of motorcyclist injury severity. For instance, the estimation results of the head-on crash model reveal that motorcyclists were more injurious in collisions where curves were present for cars than where the bend was absent. Another noteworthy result is that a traversing motorcycle colliding with a travelling-straight car predisposed motorcyclists to a greater risk of KSIs. These findings were clearly obscured by the estimation of the aggregate model by accidents in whole. In the course of the investigation of the factors that affect motorcyclist injury severity, it became clear that another problem, that of a right-turn motorist's failure to yield to motorcyclists (for the deadliest crash patterns in both approach-turn crash and angle crash), needs to be further examined. The logistic models are estimated to evaluate the likelihood of motorist's right-of-way violation over non right-of-way violation as a function of human attributes, weather/temporal factors, roadway/geometric factors, vehicle characteristics, and crash factors. The logistic models uncover the factors determining the likelihood of motorists' failure to yield. Noteworthy findings include, for instance, teenaged motorists, elderly motorists, male motorists, and professional motorists (Le., those driving heavy goods vehicles and buses/coaches) were more likely to infringe upon motorcycle's right-of-way. In addition, violation cases appeared to be more likely to occur on non built-up roadways, and during evening/midnight/early morning hours This present research has attempted to fill the research gaps that crash prediction models focused on analysing motorcyclist injury severity in different crash configurations have rarely been developed. The results obtained in this current research, by exploring a broad range of variables including attributes of riders and motorists, roadway/geometric characteristics, weather/temporal factors, and vehicle characteristics, provide valuable insights into the underlying relationship between risk factors and motorcycle injury severity both at an aggregate level and at a disaggregate level. This research finally discusses the implications of the findings and offers a guideline for future research.

The thesis reports the outcome of three main investigations. The first investigated the technical issues involved in how commuter's departure time and mode choice might be affected by road user charging. The second investigated the public's preferences for allocating revenues from a road user charging scheme. Both were based on surveys in Edinburgh, where plans for a particular scheme had been developed1. The third assessed how different charging levels affected both the demand for travel by car and the revenue generated, raising questions as to how the balance between differing objectives might be struck. The surveys undertaken used stated preference techniques to investigate departure time and mode choice changes for a sample of 211 employees working in the city centre of Edinburgh in June 2002.Departure time to work was found to be influenced not only by the work schedule (hitherto the only component of scheduling flexibility) but also by other social factors. These included whether or not regular activities were carried out before or on the way to work; and whether or not a respondent had dependent children where all adults in the household were working. The highest levels of scheduling flexibility were observed for individuals who had flexibility in both their work schedules and non-work activities. The calibrated models support the relevance of aspects of flexibility to the departure time choice. Mode choice was modelled using data from stated preference experiments. It was shown that many car drivers were prepared to switch to the bus when faced with paying a toll. Moreover, a small percentage of bus users were prepared to switch from the bus to the car, perhaps seeing an opportunistic reduction in traffic levels, which would make the car a viable alternative. Respondent's preferences for the allocation of revenues from a road user charging scheme were jointly modelled with choice of mode of travel using the nested logit model. The justification for this is that the allocation of the revenues might affect other choice decisions. The overall goodness of fit of the model was reasonable, although not all of the model coefficients were statistically significant. However, the approach to jointly modelling mode choice and revenue allocation offers an interesting concept, which is worthy of further research. The effect of different levels of charge on traffic levels and revenue raised was investigated based on the three levels of charge, which were offered to all respondents. The findings suggest that there would be a slight increase in traffic at the lowest level of charge (in this case £2), while traffic reduces significantly at each of the next two levels of charge (£3.50 and £5). On the other hand, the revenue from the scheme increases with increasing levels of charging (up to £3.50), and then decreases with the decrease in demand. Therefore, based on this pattern, alternative optimum levels of charge can be identified to achieve different objectives. For example, in this case, if the objective is to maximise revenues, then the optimum level of charging appears to be about £3.50 - somewhat above the level of charge of £2 proposed in the Edinburgh scheme. If the objective is to reduce traffic levels, that would certainly require a higher charge than £2, but what the optimum level of charging might be cannot be determined for such an objective, since the quantity demanded still appears to be falling even when the charge is £5.The main principle behind the theory of road user charging is that motorists should be charged for using the roads, where the extent of the charge reflects the costs that they impose on others and on the environment (thus helping to reduce traffic congestion). However, the experience from other studies suggests that in almost all cases the revenues generated are as relevant. These two objectives are conflicting; there is a need for further research and development of methodologies for the investigation of optimum specifications of schemes that reach a satisfactory compromise between the two objectives, taking into account the fact that raising revenue helps to provide funds that (under a hypothecated scheme) can be ploughed back into transport, and hence help reduce the demand for road use. Moreover, there is a need for an explicit recognition of the relevance of the revenues for the success of the scheme in terms of technicality and public acceptability.