Traffic congestion and road accidents are two external costs of transport and the reduction of their impacts is often one of the primary objectives for transport policy makers. The relationship between traffic congestion and road accidents however is not apparent and less studied. It is speculated that there may be an inverse relationship between traffic congestion and road accidents, and as such this poses a potential dilemma for transport policy makers. This study aims to explore the impact of traffic congestion on the frequency of road accidents using a spatial analysis approach, while controlling for other relevant factors that may affect road accidents. The M25 London orbital motorway, divided into 70 segments, was chosen to conduct this study and relevant data on road accidents, traffic and road characteristics were collected. A robust technique has been developed to map M25 accidents onto its segments. Since existing studies have often used a proxy to measure the level of congestion, this study has employed a precise congestion measurement. A series of Poisson based non-spatial (such as Poisson-lognormal and Poisson-gamma) and spatial (Poisson-lognormal with conditional autoregressive priors) models have been used to account for the effects of both heterogeneity and spatial correlation. The results suggest that traffic congestion has little or no impact on the frequency of road accidents on the M25 motorway. All other relevant factors have provided results consistent with existing studies.

There is an ongoing debate among transport planners and safety policy makers as to whether there is any association between the level of traffic congestion and road safety. One can expect that the increased level of traffic congestion aids road safety and this is because average traffic speed is relatively low in a congested condition relative to an uncongested condition, which may result in less severe crashes. The relationship between congestion and safety may not be so straightforward, however, as there are a number of other factors such as traffic flow, driver characteristics, road geometry, and vehicle design affecting crash severity. Previous studies have employed count data models (either Poisson or negative binomials and their extensions) while developing a relationship between the frequency of traffic crashes and traffic flow or density (as a proxy for traffic congestion). The use of aggregated crash counts at a road segment level or at an area level with the proxy for congestion may obscure the ...

This is an open access article under the CC BY license\ud (http://creativecommons.org/licenses/by/3.0/).Ever since the 1970s, Demand Responsive Transport (DRT) has been promoted as a transport solution in circumstances where more traditional services are not economically viable, although so far a range of barriers has prevented its widespread adoption. More recently, new developments in operational and vehicle technology, coupled with significant cuts to public transport subsidy budgets, promote a willingness to explore 'institutionally challenging' options such as integrating transport provision across a range of different sectors. This has once more pushed the DRT concept forward as a possible option for saving money whilst retaining opportunities for accessibility.Accordingly, it is now useful to explore the current provision of DRT in Great Britain, in order to determine what type of services exist and to examine which are working well and why. Specifically, the paper draws on a national survey of DRT providers to examine the design, performance, rationale and likely futures of DRT schemes.Key findings suggest a growing role for stakeholders from the voluntary sector and the private sector, the latter resulting in a greater use of smaller vehicles. Linear regression models highlight that passenger numbers are influenced by the size of operation (in terms of seats offered) and by the use of smaller 'car' vehicles, particularly in rural areas. Increasingly, objectives highlight the importance of DRT in providing access and geographical coverage, though insufficient revenue presents a challenge in achieving this. The long term financial sustainability of such schemes continues to be questioned, with a limited number of schemes recognised as commercially sustainable. Naturally, therefore, cost and funding remain dominant concerns of DRT service providers. The organisational response to funding reductions has been diverse. The result is that DRT services have either been withdrawn or, in some cases, replaced conventional bus services due to DRT being a more cost-effective way of meeting local needs

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-lognormal) for modelling 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. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naïve ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and 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.

This is the author’s version of a work that was accepted for publication in the journal Research in Transportation Business & Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published at: http://dx.doi.org/10.1016/j.rtbm.2012.04.007In principle, Demand Responsive Transport services, or paratransit in US nomenclature, offer public\ud transport providers a more flexible and potentially more cost effective delivery option than\ud conventional bus services, particularly in situations of low demand. However in practice, there are\ud many examples of promising DRT schemes that have failed, for a number of reasons. One recurring\ud feature appears to be that the DRT operation introduced is not appropriate for the market served.\ud This is due to a lack of knowledge as to what markets may be susceptible to DRT.\ud This paper aims to help address this research gap by drawing on the findings of two qualitative\ud research data collection efforts, exploratory in-depth interviews and focus groups, each including\ud industry experts. Using a marketing framework, developments at the micro, meso and macro level\ud are explored to determine the circumstances necessary for developing ‘successful’ DRT market\ud niches.\ud Implications for managerial practice include integration of services to improve market penetration\ud and in responding to market development opportunities aimed at the general public. Technology\ud plays the greatest role in responding to market niche demand, primarily in enabling flexible booking\ud and providing real time information, supporting market development, product development and\ud diversification opportunities