UK Motorway Research Articles

Level 2 Hands-Free Driving Approved For UK Motorways

Ford has been granted approval for its Level 2 hands-free advanced driver assistance system to be driven on UK motorways.

Examples of carbon dioxide emissions data in the circular economy: highway transport

The UK needs to reduce the carbon dioxide emissions of its transport sector to meet its net-zero target. This paper considers the carbon dioxide cycle for two UK motorway bridges built in the 1960s. The emissions estimates are validated using both carbon dioxide emission intensity and by comparison with published data of similar assets, while user emissions are estimated from estimates of the number of vehicles using the assets. Conclusions on past and future carbon dioxide emissions are drawn from these examples. The work confirms that driving down user-generated emissions rather than capital or operational and maintenance emissions is the key to achieving net-zero targets.

Enhancement of traffic management algorithms for UK motorways

The overall aim of this work is to review and improve traffic CM algorithm to delay the onset of flow breakdown. The results indicated that compared to other two classic models, the Underwood model was able to match the field data on the M25 motorway consistently and capture the speed-flow relationships successfully, in terms of larger R-squared coefficient (R2) and smaller average values of root mean squared error (RMSE). In addition, it was found that Gaussian function can describe the relationship between flow values at the turning points of traffic speed and flow curve and the threshold of the CM algorithm. The fitted traffic speed and flow curve showed that the values at the turning points were significantly reduced under light and heavy rainfall. Thus, the threshold values of the CM algorithm should be optimized according to the determined Gaussian function in the present work.

Quantifying fuel-saving benefit of low-rolling-resistance tyres from heavy goods vehicle in-service operations

It is important to understand real-world performance of fuel-saving technologies for heavy goods vehicles (HGVs). This paper concerns applying data mining to quantify the fuel-saving benefit of a low-rolling-resistance (LRR) tyre using in-service operational data. Two HGVs of the same specification were employed, with one using LRR tyres and the other conventional tyres. A smartphone-based data logger was developed to collect data from the HGVs’ operations on publics roads for three months. Data from vehicle Controller Area Network, smartphone sensors, weather and elevation databases were collected. A data mining methodology was developed to mine data segments representing dynamically similar driving conditions between the two HGVs, and compare their fuel consumption rates through fitting data to a theoretical vehicle fuel consumption model. It was found that at a 95% confidence level the LRR tyres exclusively brought about a fuel-saving benefit between 6.89% and 8.37% in typical UK motorway driving conditions.

Going further with smaller EVs: System-level battery range, emissions and charging infrastructure analysis

Electric vehicles are a necessary part of a zero-carbon future. However, one in five motorists worldwide depend on small petrol motorcycles for their transport needs — vehicles for which no satisfactory low-carbon substitute exists. Meanwhile, the rise in electric car ownership is not reducing GHG emissions as much as often thought, due to the significant emissions from producing ever-larger batteries. Both problems can be solved by uncovering the mechanisms of long distance EV travel, beyond battery range, where the interaction with recharging infrastructure governs vehicle performance.This study develops a new model for journeys involving multiple run-recharge cycles and introduces a novel metric for EV performance — Day Range. Not only does this allow a direct comparison between a wide variety of vehicle and infrastructure options but, by further manipulating the formulae, high level trends can be observed and specific quantitative guidelines extracted.In vehicle design, a strong emphasis on efficiency and recharge rates can drastically reduce both in-use and embodied energy while matching the touring performance of a conventional, resource intensive, heavy battery car. Meanwhile, the recharging network can be developed to better support this lower energy use. Taking the example of the UK motorway network, charge rates up to only 100kW should be installed with the focus instead falling on reliably reducing chargepoint intervals at least as far as the existing target of 28 miles, and ideally much further. In doing so, required battery capacity can be reduced from the 60kWh+ currently seen as necessary to as little as 25kWh.The resulting vehicles not only consume less energy in motion but emit far less greenhouse gases during manufacture and will cost less to produce, allowing a much wider uptake of electric vehicles than possible under the existing, energy intensive battery vehicle touring paradigm.

Perceived Safety and Driving Confidence on UK Motorways

Perceived Safety and Driving Confidence on UK Motorways

Evaluating the Barrier Effects of Charge Point Trauma on UK Electric Vehicle Growth

For electric vehicles (EVs) to realise the UK government’s goal of mass-market dominance, there are surmountable hurdles to resolve before car users accept this radical shift in motoring technology. This study focuses on recent EV adopters who experience a new phenomenon described as charge point trauma (CPT). In contrast to range anxiety, we define CPT as the psychological, physiological, and behavioural condition where EV user’s experiences develop trauma or anxiety in response to the availability of sufficient charge points, locations, payment processes, and operability. Resolving impediments to EV usage reduces long-term growth barriers, which we argue can subsequently lower or even eliminate EV driver anxiety. We conclude that range anxiety still plays a major part in overall EV driver trauma, and after deep analysis of our case study data conclude that a trauma other than range anxiety exists at the charge point. To mitigate this phenomenon, we propose a regulatory framework comprising a series of stimuli to encourage EV uptake. These recommendations should be targeted at regulating a new generation of EV charging stations to meet operational parity with current fossil fuel filling stations by ensuring they are always on, available in sufficient numbers, accessible and operable as part of the UK motorway and major trunk network. This will de-risk EV purchasing and stimulate their adoption in this embryonic stage, reducing CPT in the process.

Bayesian hierarchical models for linear networks

The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two methods have been developed to achieve this aim. First, the motorway-specific intensity is estimated by using a homogeneous Poisson process. The heterogeneity across motorways is incorporated using two-level hierarchical models. The data structure is multilevel since each motorway consists of junctions that are joined by grouped segments. In the second method, the segment-specific intensity is estimated. The homogeneous Poisson process is used to model accident data within grouped segments but heterogeneity across grouped segments is incorporated using three-level hierarchical models. A Bayesian method via Markov Chain Monte Carlo is used to estimate the unknown parameters in the models and the sensitivity to the choice of priors is assessed. The performance of the proposed models is evaluated by a simulation study and an application to traffic accidents in 2016 on the UK motorway network. The deviance information criterion (DIC) and the widely applicable information criterion (WAIC) are employed to choose between models.

Understanding the distraction and behavioural adaptations of drivers when experiencing failures of digital side mirrors

Digital mirrors within vehicles may improve aerodynamics and the field of view. Nevertheless, digital technology may fail. This study investigated the influences of different failures on distraction and behavioural adaptation, measured using glance and driving behaviour, as well as a workload questionnaire. Three failure conditions included a blank (no information), a degraded (hard to extract information), and a frozen (misleading information) display. In a high-fidelity simulator, 30 participants undertook three drives in a UK motorway scenario. During the second drive, the right (offside) digital mirror failed during the instruction to conduct a left–right lane-change manoeuvre, and remained until the end of that drive. Results show that failures led to longer and more glances towards the driver-side mirror, increased variability of speed and lateral position, and heightened workload; however, these distracting and behavioural effects lessened in future drives. Behavioural adaptations in the form of increased rear-view mirror or blind-spot checks could not be established. There were indications that the blanked mirror affected workload less than the other failures.

Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles

Today’s market leading electric vehicles, driven on typical UK motorways, have real-world range estimation inaccuracy of up to 27%, at around 10 °C outside temperature. The inaccuracy worsens for city driving or lower outside temperature. The reliability of range estimation largely depends on the accuracy of the battery’s underlying state estimators, e.g., state-of-charge and state-of-energy. This is affected by accuracy of the models embedded in the battery management system. The performance of these models fundamentally depends on experimentally obtained parameterisation and validation data. These experiments are mostly performed within thermal chambers, which maintain pre-set temperatures using forced air convection. Although these setups claim to maintain isothermal test conditions, they rarely do so. In this paper, we show that this is potentially the root-cause for deterioration of range estimation at low temperatures. This is because, while such setups produce results comparable to isothermal conditions at higher temperatures (25 °C), they fail to achieve isothermal conditions at sub-zero temperatures. Employing an immersed oil-cooled experimental setup, which can create close-to isothermal conditions, we show battery state estimation can be improved by reducing error from 49.3% to 11.7% at −15 °C. These findings provide a way forward towards improving range estimation in cold weather conditions.

Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set

Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still working on novel approaches. This study aims to develop an algorithm that dynamically updates the training set of models in order to make more accurate predictions. For this purpose, an algorithm called Periodic Clustering and Prediction (PCP) has been developed for use in short-term traffic forecasting. In this study, PCP was used to improve Artificial Neural Networks (ANN) predictive performance by improving the training set of ANN to predict short-term traffic flow using selected clusters. A large amount of traffic data collected from the US and UK motorways was used to determine the PCP ability to increase the ANN performance. The robustness of the proposed approach was determined by the performance measures used in the literature and the mean prediction errors of PCP were significantly below other approaches. In addition, the studies showed that the percentage errors of PCP predictions decreased in response to increasing traffic flow values. Considering the obtained positive results, this method can be used in real-time traffic control systems and in different areas needed.

Development of a Microsimulation Model for Motorway Roadworks With Narrow Lanes

This paper presents a newly developed microsimulation model for motorway roadwork sections to evaluate the efficiency of different temporary traffic management schemes (TTMSs) such as the use of narrow lanes, offside and inside lane closures. The effect on traffic performance of various parameters (e.g. flow rates, percentage of heavy goods vehicles (HGVs), roadwork zone lengths and speed limits) has been tested. The model was built using the FORTRAN programing language. It was developed based on car-following, discretionary lane changing, mandatory lane changing, gap acceptance and narrow lanes rules. Data from four sources (including data taken from different UK motorway sites) were collected and analyzed. The data were used in the verification, calibration and validation processes of the model. Observations from motorway roadwork sites with the narrow lanes scheme show certain prominent drivers’ behaviors, namely avoiding passing HGVs traveling on adjacent lanes and lane repositioning before passing a HGV. Such behaviors were included in the modeling process which suggested that the presence of HGVs had a noticeable impact on reducing section capacity.

Pavement layer thickness variability evaluation and effect on performance life

ABSTRACTThis paper aims to assess the sensitivity of road pavement design and predicted life to pavement layer thickness, accounting for the variations which commonly occur between the pavement design thickness specified and that actually constructed. The approach adopted in this research is to characterise the as-built pavement layer thickness variation, obtained from Ground Penetrating Radar (GPR) surveys, for lane 1 of a UK motorway site with flexible pavement construction. Firstly, an analysis is conducted for the selected UK motorway to explore the relationship between the specified construction tolerances and the actual thickness variability. Secondly, the paper proves how variability of layer thickness – while keeping all the other design parameters unchanged – affects the design life probability distribution. The results from this study show that asphalt layer thickness variation significantly affects the pavement performance, even when such variations are within the tolerances allowed by UK construction specifications. On the other hand, layer thickness variations in granular sub-base, within allowed limits, do not significantly affect the pavement performance.

Driven to discussion: engaging drivers in conversation with a digital assistant as a countermeasure to passive task‐related fatigue

Using a Wizard-of-Oz approach, we explored the effectiveness of engaging drivers in conversation with a digital assistant as an operational strategy to combat the symptoms of passive task-related fatigue. Twenty participants undertook two 30-minute drives in a medium-fidelity driving simulator between 13:00 and 16:30, when circadian and homeostatic influences naturally reduce alertness. Participants were asked to follow a lead-car travelling at a constant speed of 68mph, in a sparsely-populated UK motorway scenario. During one of the counterbalanced drives, participants were engaged in conversation by a digital assistant (‘Vid’). Results show that interacting with Vid had a positive effect on driving performance and arousal, evidenced by better lane-keeping, earlier response to a potential hazard situation, larger pupil diameter, and an increased spread of attention to the road-scene (i.e. fewer fixations concentrated on the road-centre indicating a lower incidence of ‘cognitive tunnelling’). Drivers also reported higher levels of alertness and lower sleepiness following the Vid drive. Subjective workload ratings suggest that drivers exerted less effort to ‘stay awake’ when engaged with Vid. The findings support the development and application of in-vehicle natural language interfaces, and can be used to inform the design of novel countermeasures for driver fatigue.

Reliability and quality of service of an off-grid wind powered roadside unit in a motorway vehicular environment

Wind-powered base stations and roadside units have been considered as a cost effective greening solution in windy countries which also have limited solar irradiation. The practicality of such a system increases significantly in sparse areas such as countryside and motorways. The deployment of standalone off-grid wind powered roadside units could alleviate the common issues related to grid connected renewable energy farms. Hence, there is need to study the feasibility of an off-grid wind powered roadside unit for seamless connectivity. Unlike the conventional usage of reliability analysis of fault-tolerant systems, in this paper, reliability is redefined in the context of availability of intermittent wind for powering a roadside unit (RSU) in a UK motorway vehicular environment. Transient analysis of energy consumption (energy demand) of the RSU and harnessed wind energy are carried out along with real measurements for developing respective generic energy models. Further, a generalised methodology is developed to determine the minimum battery size for achieving a certain reliability standard and quality of service. Several reliability indices such as loss of load probability (LOLP), loss of load expectation (LOLE), energy index of reliability (EIR), mean time between failures (MTBF), mean time to recovery (MTTR), forced outage rate (FOR), etc. are obtained for the RSU. The performance results reveal that with a standard micro-turbine and a reasonably small battery, an RSU achieves a good reliability of 99.9% with significant improvement in the quality of service.

Narrow lanes and their effect on drivers’ behaviour at motorway roadworks

In recent years, more use of narrow lanes as a temporary traffic management scheme (TTMS) on UK motorway roadwork sections has been made. The rationale is to free up carriageway space, especially for sites with high traffic demands needing repairs. What remains to be determined is the impact of this work on traffic operation. This is important due to the need to manage traffic operational turbulence which could affect the capacity as well as safety levels in roadwork sections. Site observations (mainly using camcorders from overhead bridges) were made which uncovered two discernible patterns of driving behaviour where narrow lanes are implemented at roadworks, especially when heavy goods vehicles (HGVs) are present: (i) “avoiding” passing/overtaking HGVs travelling in the adjacent lanes and (ii) lane “repositioning” while passing/overtaking which might cause some turbulence to traffic operation. From one site, almost half of the passenger car drivers (including small vans) who were following a HGV on the adjacent lanes were avoiding passing that HGV. Also, nearly three quarters of the observed passenger cars, the passenger car drivers tried to position their vehicles as far away as possible, laterally, from the adjacent HGV while passing/overtaking that HGV in order to widen the lateral gap between their vehicles and the HGV. This resulted in driving too close to the edge of the road markings of their current lane away from the HGV. Therefore, this paper aims to report on the “avoiding” and lane “repositioning” behaviours to help inform traffic management teams/designers using narrow lanes as TTMS and make them aware of such behaviours (especially for motorway sections carrying high percentages of HGVs). Also, the finding from these observations were used in the development of a new micro-simulation model in order to evaluate the effect of such turbulence.

Transferability of Car-Following Models Between Driving Simulator and Field Traffic

During the past few decades, there have been two parallel streams of driving behavior research: models using trajectory data collected from the field (using video recordings, GPS, etc.) and models using data from driving simulators (in which the behavior of the drivers is recorded in controlled laboratory conditions). Although the former source of data is more realistic, it lacks information about the driver and is typically not suitable for testing effects of future vehicle technologies and traffic scenarios. In contrast, driving behavior models developed with driving simulator data may lack behavioral realism. However, no previous study has compared these two streams of mathematical models and investigated the transferability of the models developed with driving simulator data to real field conditions in a rigorous manner. The current study aimed to fill this research gap by investigating the transferability of two car-following models between a driving simulator and two comparable real-life traffic motorway scenarios, one from the United Kingdom and the other one from the United States. In this regard, stimulus–response–based car-following models were developed with three microscopic data sources: ( a) experimental data collected from the University of Leeds driving simulator, ( b) detailed trajectory data collected from UK Motorway 1, and ( c) detailed trajectory data collected from Interstate 80 in California. The parameters of these car-following models were estimated by using the maximum likelihood estimation technique, and the transferability of the models was investigated by using statistical tests of parameter equivalence and transferability test statistics. Estimation results indicate transferability at the model level but not fully at the parameter level for both pairs of scenarios.

Driving without wings: The effect of different digital mirror locations on the visual behaviour, performance and opinions of drivers

Drivers' awareness of the rearward road scene is critical when contemplating or executing lane-change manoeuvres, such as overtaking. Preliminary investigations have speculated on the use of rear-facing cameras to relay images to displays mounted inside the car to create ‘digital mirrors'. These may overcome many of the limitations associated with traditional ‘wing’ and rear-view mirrors, yet will inevitably effect drivers' normal visual scanning behaviour, and may force them to consider the rearward road scene from an unfamiliar perspective that is incongruent with their mental model of the outside world. We describe a study conducted within a medium-fidelity simulator aiming to explore the visual behaviour, driving performance and opinions of drivers while using internally located digital mirrors during different overtaking manoeuvres. Using a generic UK motorway scenario, thirty-eight experienced drivers conducted overtaking manoeuvres using each of five different layouts of digital mirrors with varying degrees of ‘real-world’ mapping. The results showed reductions in decision time for lane changes and eyes-off road time while using the digital mirrors, when compared with baseline traditional reflective mirrors, suggesting that digital displays may enable drivers to more rapidly pick up the salient information from the rearward road scene. Subjectively, drivers preferred configurations that most closely matched existing mirror locations, where aspects of real-world mapping were largely preserved. The research highlights important human factors issues that require further investigation prior to further development/implementation of digital mirrors within vehicles. Future work should also aim to validate findings within real-world on-road environments whilst considering the effects of digital mirrors on other important visual behaviour characteristics, such as depth perception.

Short Term Traffic Prediction on the UK Motorway Network Using Neural Networks

To be able to predict reliably traffic conditions over the short term (15 minutes into the future) may reduce congestion on a transport system. With the emergence of large datasets comes the opportunity to test the effectiveness of pattern recognition techniques to solve complex, non-linear problems such as the one in question.This paper presents the results of applying artificial intelligence, specifically artificial neural networks (ANNs), to estimate traffic conditions a 15 minutes into the future given current / historic traffic information. For this study, data from Highways England's Motorway Incident Detection and Automatic Signalling (MIDAS) system for approximately 20 km of the M60, M62 and M602 motorway near Manchester, UK was used to build a short term prediction model. To reduce the complexity of the problem, the number of input dimensions to the model was successfully reduced using an autoencoder. The final model exhibits very good predictive power with 90% of all predictions within 2.6 veh/km/lane of observed values.The approach adopted in this research is one that can be transferred to other parts of the UK motorway network where MIDAS is installed, and once trained, the application of an ANN is straightforward. An algorithm such as the one derived has multiple applications including: refining predictions within intelligent transport systems (ITS) and / or enabling traffic controllers to take proactive decisions to mitigate the impacts of expected congestion. It could also be the engine behind a “traffic-cast” system which could provide the public with a forecast of expected traffic conditions. This could result in reduced congestion on the transport system as accessibility to more accurate information could encourage beneficial behavioural changes in users.

Predicting the safety impact of a speed limit increase using condition-based multivariate Poisson lognormal regression

ABSTRACTSpeed limit changes are considered to lead to proportional changes in the number and severity of crashes. To predict the impact of a speed limit alteration, it is necessary to define a relationship between crashes and speed on a road network. This paper examines the relationship of crashes with speed, as well as with other traffic and geometric variables, on the UK motorways in order to estimate the impact of a potential speed limit increase from 70 to 80 mph on traffic safety. Full Bayesian multivariate Poisson lognormal regression models are applied to a data set aggregated using the condition-based approach for crashes by vehicle (i.e. single vehicle and multiple vehicle) and severity (i.e. fatal or serious and slight). The results show that single-vehicle crashes of all severities and fatal or serious injury crashes involving multiple vehicles increase at higher speed conditions and particularly when these are combined with lower volumes. Slight injury multiple-vehicle crashes are found not to be related to high speeds, but instead with congested traffic. Using the speed elasticity values derived from the models, the predicted annual increase in crashes after a speed limit increase on the UK motorway is found to be 6.2–12.1% for fatal or serious injury crashes and 1.3–2.7% for slight injury, or else up to 167 more crashes.