Comparison of homogeneous and heterogeneous motorised traffic at signalised and two-way stop control single lane intersection

Results of a microscopic model of mixed motorised traffic consisting of short vehicles, (e.g. cars), and long vehicles, (taken to be double the length of the short vehicles), for an urban two-way single lane intersection are presented here. We model the intersection using both signalised and un-signalised stop control rules. The model allows for the detection of bottleneck activity in both homogenous and heterogeneous traffic conditions, and was validated by means of field data collected in Dublin, Ireland. The validated model was used to study the impact of inclusion of long vehicles on traffic performance in an urban environment. Traffic mix is, however, taken to be dominated by short vehicles overall, in argument with observed live data collected

Heterogenous motorised traffic flow modelling using cellular automata

Traffic congestion is a major problem in most major cities around the world with few signs that this is diminishing, despite management efforts. In planning traffic management and control strategies at urban and inter urban level, understanding the factors involved in vehicular progression is vital. Most work to date has, however, been restricted to single vehicle-type traffic. Study of heterogeneous traffic movements for urban single and multi-lane roads has been limited, even for developed countries and motorised traffic mix, (with a broader spectrum of vehicle type applicable for cities in the developing world). The aim of the research, presented in this thesis, was thus to propose and develop a model for heterogeneous motorised traffic, applicable to situations, involving common urban and interurban road features in the western or developed world. A further aim of the work was to provide a basis for comparison with current models for homogeneous vehicle type. A two-component cellular automata (2-CA) methodology is used to examine traffic patterns for single-lane, multi-lane controlled and uncontrolled intersections and roundabouts. In this heterogeneous model (binary mix), space mapping rules are used for each vehicle type, namely long (double-unit length) and short (single-unit length) vehicles. Vehicle type is randomly categorised as long (LV) or short (SV) with different fractions considered. Update rules are defined based on given and neighbouring cell states at each time step, on manoeuvre complexity and on acceptable space criteria for different vehicle types. Inclusion of heterogeneous traffic units increases the algorithm complexity as different criteria apply to different cellular elements, but mixed traffic is clearly more reflective of the real-world situation. The impact of vehicle mix on the overall performance of an intersection and roundabout (one-lane one-way, one-lane two-way and two-lane two-way) has been examined. The model for mixed traffic was also compared to similar models for homogeneous vehicle type, with throughput, queue length and other metrics explored. The relationship between arrival rates on the entrance roads and throughput for mixed traffic was studied and it was found that, as for the homogeneous case, critical arrival rates can be identified for various traffic conditions. Investigation of performance metrics for heterogeneous traffic (short and long vehicles), can be shown to reproduce main aspects of real-world configuration performance. This has been validated, using local Dublin traffic data. The 2-CA model can be shown to simulate successfully both homogeneous and heterogeneous traffic over a range of parameter values for arrival, turning rates, different urban configurations and a distribution of vehicle types. The developed model has potential to extend its use to linked transport network elements and can also incorporate further motorised and non-motorised vehicle diversity for various road configurations. It is anticipated that detailed studies, such as those presented here, can support efforts on traffic management and aid in the design of optimisation strategies for traffic flow

Simulation of heterogeneous motorised traffic at a signalised intersection

The characteristics of heterogeneous traffic (with variation in vehicle length) are significantly different from those for homogeneous traffic. The present study describes an overview of the development and validation of a stochastic heterogeneous traffic-flow simulation model for an urban single-lane two-way road, with controlled intersection. In this paper, the interaction between vehicle types during manoeuvres at the intersection are analysed in detail. Two different motorised vehicle types are considered namely cars and buses (or similar length vehicles). A two-component cellular automata (CA) based model is used. Traffic flow data, captured manually by Dublin City Council at a local intersection, are analysed to give a baseline on how the distribution of short and long vehicles affect throughput. It is anticipated that such detailed studies will aid traffic management and optimisation strategies for traffic flow

Engineering Countermeasures for Left Turns at Signalized Intersections: A Review

Left turn crashes can impact the safety of the drivers due to the speed and angle at which they occur. Left turns are specifically reported to affect older drivers more than the other types of crashes. This paper provides a review of the existing engineering countermeasures that have been evaluated to improve driver safety at left turns. Twenty- eight studies on left turn signal displays (protected left turns, flashing yellow arrow, and digital countdown timers), intersection geometry (offset left turn lanes, diverging diamond interchange, roundabouts, exit lanes for left turn, left turn bay extension, and contraflow left turn lanes), and driver warning systems (infrastructure warning systems, and in-vehicle warning systems) are reviewed. Eighteen studies were evaluated in the field, nine in laboratory environments, and one online. All countermeasures demonstrated varying levels of effectiveness. We found protected left turns, roundabouts, and warning systems to be the most effective engineering countermeasures. Advantages and disadvantages of each countermeasure and research shortcomings of the evaluation studies are discussed. Review findings may help practitioners and researchers guide more effective countermeasures for left turns for older drivers

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCube

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCub

Explanation of factors influencing cyclists’ route choice using actual route data from cyclists

Cycling as a sustainable means of transport brings a number of benefits, which includes improved health and well-being for individuals, improved air quality and climate change, accessibility and reduced traffic congestion at the national level. However, despite the benefits of cycling and the efforts by the government to promote this mode of transport, many short trips in Britain suitable for cycling are still made by motorised transport modes. People seem reluctant to change their mode of travel behaviour in favour of cycling. Therefore, it is important to understand the nature of complicated behaviour of people and the ones of cyclists at first. The thesis aimed to understand route choice behaviour for cycling for utility purposes in England. The thesis examined why cyclists use their current routes and how various features influence their choices. The thesis also probed the reasons for the choices and the relationship between the choice and the characteristics of cyclists. A mixed method approach was applied for the thesis, using questionnaires, actual route data collection for quantitative methods and interviews for qualitative methods. This approach allowed the researcher to examine diverse aspects of the research questions, which individual methods were unlikely to address. The thesis has identified what route features are important for cyclists, and why these features are considered important. In terms of the issues regarding cycling infrastructures, the preferences of cyclists were found to be linked to the fear to motorised traffic on roads, which is a fundamental issue that may not be revealed through quantitative studies. Another key finding identified was that cyclists choose different routes dependent on the conditions applicable even for same trip purposes. In this respect, it was noted that often their choices are forced by prevailing road instructions such as one-way road, although they may be aware that the alternative road conditions may not be good from a cycling viewpoint. However, it was also found that, where practicable, cyclists are likely to choose a route strategically, in a manner that will minimise the physical efforts required for cycling. Finally, based on the observations of the different geographical and environmental characteristics and atmosphere to cycling in two case study cities, the thesis also discovered the segment of the population who could become the main target for promoting the benefits of cycling

Evaluating changes in driver behaviour for road safety outcomes: a risk profiling approach

Road safety continues to be an important issue with road crashes among the leading causes of death. Considerable effort has been put into improving our understanding of the factors that influence driving behaviour with a view to devising more effective road safety strategies. Within the literature, demographics, social norms, personality, enforcement and the road environment have all been identified as influencers of risky driving behaviour. What is missing is an integrated empirical approach which examines the relationship between these factors and drivers’ awareness of their speeding behaviour to a measure of day-to-day driving behaviour. This research employs demographic, psychological, vehicle, trip and Global Positioning System (GPS) driving data collected from 106 drivers in Sydney, Australia during a pay-as-you-drive study. The main contributions are three-fold. First, a methodology is developed to control for the influence of spatiotemporal characteristics on driver behaviour. This deals with the inherent variability introduced from road environment factors external to the driver which would otherwise lead to misleading results. Second, the creation of a composite measure of driver behaviour allows driver behaviour to be described using a single measure whilst accounting for the variability and multitude of aspects within the driving task. This allows drivers to be compared to each other and for the same driver to be compared across time and space permitting empirical testing of interventions in a before and after study. Lastly, this research reveals the potential for reducing the extent and magnitude of risky driving behaviour by making drivers aware of their own behaviour. The results indicate that drivers can be placed in three groups: drivers requiring a monetary incentive to change speeding behaviour, drivers requiring information alone to change their speeding behaviour and drivers that appear unresponsive to both monetary incentives and information

Identification of infrastructure related risk factors, Deliverable 5.1 of the H2020 project SafetyCube

The present Deliverable (D5.1) describes the identification and evaluation of infrastructure related risk factors. It outlines the results of Task 5.1 of WP5 of SafetyCube, which aimed to identify and evaluate infrastructure related risk factors and related road safety problems by (i) presenting a taxonomy of infrastructure related risks, (ii) identifying “hot topics” of concern for relevant stakeholders and (iii) evaluating the relative importance for road safety outcomes (crash risk, crash frequency and severity etc.) within the scientific literature for each identified risk factor. To help achieve this, Task 5.1 has initially exploited current knowledge (e.g. existing studies) and, where possible, existing accident data (macroscopic and in-depth) in order to identify and rank risk factors related to the road infrastructure. This information will help further on in WP5 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures. In order to develop a comprehensive taxonomy of road infrastructure-related risks, an overview of infrastructure safety across Europe was undertaken to identify the main types of road infrastructure-related risks, using key resources and publications such as the European Road Safety Observatory (ERSO), The Handbook of Road Safety Measures (Elvik et al., 2009), the iRAP toolkit and the SWOV factsheets, to name a few. The taxonomy developed contained 59 specific risk factors within 16 general risk factors, all within 10 infrastructure elements. In addition to this, stakeholder consultations in the form of a series of workshops were undertaken to prioritise risk factors (‘hot topics’) based on the feedback from the stakeholders on which risk factors they considered to be the most important or most relevant in terms of road infrastructure safety. The stakeholders who attended the workshops had a wide range of backgrounds (e.g. government, industry, research, relevant consumer organisations etc.) and a wide range of interests and knowledge. The identified ‘hot topics’ were ranked in terms of importance (i.e. which would have the greatest effect on road safety). SafetyCube analysis will put the greatest emphasis on these topics (e.g. pedestrian/cyclist safety, crossings, visibility, removing obstacles). To evaluate the scientific literature, a methodology was developed in Work Package 3 of the SafetyCube project. WP5 has applied this methodology to road infrastructure risk factors. This uniformed approach facilitated systematic searching of the scientific literature and consistent evaluation of the evidence for each risk factor. The method included a literature search strategy, a ‘coding template’ to record key data and metadata from individual studies, and guidelines for summarising the findings (Martensen et al, 2016b). The main databases used in the WP5 literature search were Scopus and TRID, with some risk factors utilising additional database searches (e.g. Google Scholar, Science Direct). Studies using crash data were considered highest priority. Where a high number of studies were found, further selection criteria were applied to ensure the best quality studies were included in the analysis (e.g. key meta-analyses, recent studies, country origin, importance). Once the most relevant studies were identified for a risk factor, each study was coded within a template developed in WP3. Information coded for each study included road system element, basic study information, road user group information, study design, measures of exposure, measures of outcomes and types of effects. The information in the coded templates will be included in the relational database developed to serve as the main source (‘back end’) of the Decision Support System (DSS) being developed for SafetyCube. Each risk factor was assigned a secondary coding partner who would carry out the control procedure and would discuss with the primary coding partner any coding issues they had found. Once all studies were coded for a risk factor, a synopsis was created, synthesising the coded studies and outlining the main findings in the form of meta-analyses (where possible) or another type of comprehensive synthesis (e.g. vote-count analysis). Each synopsis consists of three sections: a 2 page summary (including abstract, overview of effects and analysis methods); a scientific overview (short literature synthesis, overview of studies, analysis methods and analysis of the effects) and finally supporting documents (e.g. details of literature search and comparison of available studies in detail, if relevant). To enrich the background information in the synopses, in-depth accident investigation data from a number of sources across Europe (i.e. GIDAS, CARE/CADaS) was sourced. Not all risk factors could be enhanced with this data, but where it was possible, the aim was to provide further information on the type of crash scenarios typically found in collisions where specific infrastructure-related risk factors are present. If present, this data was included in the synopsis for the specific risk factor. After undertaking the literature search and coding of the studies, it was found that for some risk factors, not enough detailed studies could be found to allow a synopsis to be written. Therefore, the revised number of specific risk factors that did have a synopsis written was 37, within 7 infrastructure elements. Nevertheless, the coded studies on the remaining risk factors will be included in the database to be accessible by the interested DSS users. At the start of each synopsis, the risk factor is assigned a colour code, which indicates how important this risk factor is in terms of the amount of evidence demonstrating its impact on road safety in terms of increasing crash risk or severity. The code can either be Red (very clear increased risk), Yellow (probably risky), Grey (unclear results) or Green (probably not risky). In total, eight risk factors were given a Red code (e.g. traffic volume, traffic composition, road surface deficiencies, shoulder deficiencies, workzone length, low curve radius), twenty were given a Yellow code (e.g. secondary crashes, risks associated with road type, narrow lane or median, roadside deficiencies, type of junction, design and visibility at junctions) seven were given a Grey code (e.g. congestion, frost and snow, densely spaced junctions etc.). The specific risk factors given the red code were found to be distributed across a range of infrastructure elements, demonstrating that the greatest risk is spread across several aspects of infrastructure design and traffic control. However, four ‘hot topics’ were rated as being risky, which were ‘small work-zone length’, ‘low curve radius’, ‘absence of shoulder’ and ‘narrow shoulder’. Some limitations were identified. Firstly, because of the method used to attribute colour code, it is in theory possible for a risk factor with a Yellow colour code to have a greater overall magnitude of impact on road safety than a risk factor coded Red. This would occur if studies reported a large impact of a risk factor but without sufficient consistency to allocate a red colour code. Road safety benefits should be expected from implementing measures to mitigate Yellow as well as Red coded infrastructure risks. Secondly, findings may have been limited by both the implemented literature search strategy and the quality of the studies identified, but this was to ensure the studies included were of sufficiently high quality to inform understanding of the risk factor. Finally, due to difficulties of finding relevant studies, it was not possible to evaluate the effects on road safety of all topics listed in the taxonomy. The next task of WP5 is to begin identifying measures that will counter the identified risk factors. Priority will be placed on investigating measures aimed to mitigate the risk factors identified as Red. The priority of risk factors in the Yellow category will depend on why they were assigned to this category and whether or not they are a hot topic