Equilibrium analysis of trip chains in congested networks

In this paper, we develop a model of travel in a chain of trips joining several locations througha congested network. We develop a microscopic analysis of individual benefits obtained byspending time at each of the locations and costs incurred through travel between them. This iscombined with a macroscopic equilibrium model of travel during congested peak periods toshow how individuals? travel choices are influenced by the congestion that result fromcorresponding choices made by others. We show how different travellers can achieveidentical net utilities by making different combinations of choices within the equilibrium. Theresulting model can be used to investigate the effect on travel behaviour and individual utilityof various transport interventions, and we illustrate this by considering the effect of a peakperiodcharge that eliminates congestion

Adaptive signal control using approximate dynamic programming

This paper presents a concise summary of a study on adaptive traffic signal controller for real time operation. The adaptive controller is designed to achieve three operational objectives: first, the controller adopts a dual control principle to achieve a balanced influence between immediate cost and long-term cost in operation; second, controller switches signals without referring to a preset plan and is acyclic; third, controller adjusts its parameters online to adapt new environment. Not all of these features are available in existing operational controllers. Although dynamic programming (DP) is the only exact solution for achieving the operational objectives, it is usually impractical for real time operation because of demand in computation and information. To circumvent the difficulties, we use approximate dynamic programming (ADP) in conjunction with online learning techniques. This approach can substantially reduce computational burden by replacing the exact value function of DP with a continuous linear approximation function, which is then updated progressively by online learning techniques. Two online learning techniques, which are reinforcement learning and monotonicity approximation respectively, are investigated. We find in computer simulation that the ADP controller leads to substantial savings in vehicle delays in comparison with optimised fixed-time plans. The implications of this study to traffic control are: the ADP controller meet all of the three operational objectives with competitive results, and can be readily implemented for operations at both isolated intersection and traffic networks; the ADP algorithm is computationally efficient, and the ADP controller is an evolving system that requires minimum human intervention; the ADP technique offers a flexible theoretical framework in which a range of functional forms and learning techniques can be further studied

Discrete choice modelling incorporating attribute thresholds of perception

In this paper we formulate a discrete choice model that incorporates thresholds in the perception of attribute changes. The model considers multiple options and allows changes in several attributes. We postulate that if thresholds exist they could be random, differ between individuals, and even be a function of socio-economic characteristics and choice conditions. Our formulation allows estimation of the parameters of the threshold probability distribution starting from information about choices. The model is applied to synthetic data and also to real data from a stated preference survey. We found that where perception thresholds exist in the population, the use of models without them leads to errors in estimation and prediction. Clearly, the effect is more relevant when the typical size of change in the attribute value is comparable with the threshold, and when the contribution of this attribute in the utility function is substantial. Finally, we discuss the implications of the threshold model for estimation of the benefits of transport investments, and show that where thresholds exist, models that do not represent them can overestimate benefits substantially

Video vehicle detection at signalised junctions: a simulation-based study

Many existing advanced methods of traffic signal control depend on information about approaching traffic provided by inductive loop detectors at particular points in the road. But analysis of images from CCTV cameras can in principle provide more comprehensive information about traffic approaching and passing through junctions, and cameras may be easier to install and maintain than loop detectors, and some systems based on video detection have already been in use for some time. Against this background, computer simulation has been used to explore the potential of existing and immediately foreseeable capability in automatic on-line image analysis to extract information relevant to signal control from images provided by cameras mounted in acceptable positions at signal-controlled junctions. Some consequences of extracting relevant information in different ways were investigated in the context of an existing detailed simulation model of vehicular traffic moving through junctions under traffic-responsive signal control, and the development of one basic and one advanced algorithm for traffic-responsive control. The work was confined as a first step to operation of one very simple signalcontrolled junction. Two techniques for extraction of information from images were modelled - a more ambitious technique based on distinguishing most of the individual vehicles visible to the camera, and a more modest technique requiring only that the presence of vehicles in any part of the image be distinguished from the background scene. In the latter case, statistical modelling was used to estimate the number of vehicles corresponding to any single area of the image that represents vehicles rather than background. At the simple modelled junction, each technique of extraction enabled each of the algorithms for traffic-responsive control of the signals to achieve average delays per vehicle appreciably lower than those given by System D control, and possibly competitive with those that MOVA would give, but comparison with MOVA was beyond the scope of the initial study. These results of simulation indicate that image analysis of CCTV pictures should be able to provide sufficient information in practice for traffic-responsive control that is competitive with existing techniques. Ways in which the work could be taken further were discussed with practitioners, but have not yet been progressed

Adaptive railway traffic control using approximate dynamic programming

This study presents an adaptive railway traffic controller for real-time operations based on approximate dynamic programming (ADP). By assessing requirements and opportunities, the controller aims to limit consecutive delays resulting from trains that entered a control area behind schedule by sequencing them at critical locations in a timely manner, thus representing the practical requirements of railway operations. This approach depends on an approximation to the value function of dynamic programming after optimisation from a specified state, which is estimated dynamically from operational experience using reinforcement learning techniques. By using this approximation, the ADP avoids extensive explicit evaluation of performance and so reduces the computational burden substantially. In this investigation, we explore formulations of the approximation function and variants of the learning techniques used to estimate it. Evaluation of the ADP methods in a stochastic simulation environment shows considerable improvements in consecutive delays by comparison with the current industry practice of First-Come-First-Served sequencing. We also found that estimates of parameters of the approximate value function are similar across a range of test scenarios with different mean train entry delays

Non-recurrent traffic congestion detection on heterogeneous urban road networks

This paper proposes two novel methods for non-recurrent congestion (NRC) event detection on heterogeneous urban road networks based on link journey time (LJT) estimates. Heterogeneity exists on urban road networks in two main aspects: variation in link lengths and data quality. The proposed NRC detection methods are referred to as percentile-based NRC detection and space–time scan statistics (STSS) based NRC detection. Both of these methods capture the heterogeneity of an urban road network by modelling the LJTs with a lognormal distribution. Empirical analyses are conducted on London's urban road network consisting of 424 links for the 20 weekdays of October 2010. Various parameter settings are tested for both of the methods, and the results favour STSS-based NRC detection method over the percentile-based NRC detection method. Link-based analyses demonstrate the effectiveness of the proposed methods in capturing the heterogeneity of the analysed road network

Data-driven models for microscopic vehicle emissions

In this paper, a new approach for describing the relationship between tailpipe emissions and vehicle movement variables is presented, called generalized additive model for location, scale and shape (GAMLSS). The dataset for this model is second-by-second emission laboratory measurements, following a real driving cycle that were recorded in urban, suburban and motorway areas of London. The GAMLSS emission model estimates each of CO_{2}, CO and NO_{x} in each second for two different vehicle types (petrol or diesel) using instantaneous speed and acceleration as the explanatory variables. Comparing the results with current emission models indicates substantial improvement in accuracy and quality of estimation by this approach

Fatality rates associated with driving and cycling for all road users in Great Britain 2005–2013

Fatality rates based on deaths only to the drivers themselves do not accurately portray the impact of driving on road traffic deaths. We characterised more fully the impact of driving and cycling on road traffic fatalities by including deaths to all the other road users in fatal car or cycle crashes. We used crash data from the Great Britain National Road Accident Database (STATS19) and exposure data from the National Travel Survey. Rates were estimated as the ratio of fatalities to the amount of time travelled: fatalities per million hours’ use (f/mhu). Rates in 2005-07, 2008-10, and 2011-13 were calculated based on deaths to: (1) the drivers or cyclists themselves (persons ‘in charge’ of vehicles), (2) other, i.e. ‘third-party’, road users (e.g. passengers, drivers or riders of other vehicles, and pedestrians), and (3) both of these groups combined, i.e. all road users. Rates were stratified by the sex and age of the drivers or cyclists involved in the fatal crashes. Rates based on deaths to persons in charge of vehicles were higher for cyclists than for drivers, whereas those based on deaths to third-party road users showed the opposite. The inclusion of third-party deaths increased the overall rates considerably more for drivers than for cyclists. Nevertheless, the overall rate for male cyclists (2011-13: 0.425 f/mhu; 95% Confidence Interval (CI): 0.377–0.478) exceeded that for male drivers (0.257 f/mhu; 95% CI: 0.248–0.267). A similar pattern was observed for females (cycling: 0.216 f/mhu; 95% CI: 0.158–0.287; driving: 0.127 f/mhu; 95% CI: 0.120–0.135). These differences between cars and cycles were overestimated as the safer travel on motorways could not be disaggregated in the estimates for driving. The higher rates for cycling - mainly borne by the cyclists themselves - need to be balanced against the substantially lower risks to other road users

Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks

Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on urban road networks. In this paper we propose an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined as those LJTs that are greater than a threshold. The threshold is calculated by multiplying the expected LJTs with a congestion factor. To evaluate the effectiveness of the proposed NRC detection method, we propose two novel criteria. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be associated with incidents. The proposed NRC detection methodology is tested for London's urban road network. The optimum value of the congestion factor is determined by sensitivity analysis by using a Weighted Product Model (WPM). It is found out those LJTs that are at least 40% higher than their expected values should belong to an NRC; as such NRCs are found to maintain the best balance between the proposed evaluation criteria