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The London congestion charging (LCC) scheme was first introduced in 2003. It did not only help alleviate traffic congestion and reduce vehicle emissions, but also had favorable safety effect. Western Extension of LCC was applied in 2007, but then removed in 2011. It was suggested that adjacent areas of the congestion charging zone could also be benefited. As well, the benefits would not disappear immediately after the removal of congestion charging scheme, which is known as residual effect. This paper attempts to examine the affected area and residual period of the safety benefits by the LCC scheme using the traffic and crash data from 352 Middle Super Output Areas (MSOAs) of London, with which the original LCC scheme was imposed in 24 MSOAs (‘treatment’ units for Analysis I), the Western Extension scheme was imposed in 27 MSOAs (‘treatment’ units for Analysis II), and no congestion charging was implemented at all in 301 MSOAs (‘control’ units for both Analysis I and II). Factors including traffic flow, land use, built environment and population demographics are considered. To eliminate the bias by the selection of treatment and control groups, Propensity Score matching (PSM) method is applied. Results indicate that favorable effect on safety is prevalent in the 1.5 km buffer area of LCC zone. On the other hand, for the residual effect, considerable crash reduction could be found in the first year after the removal of Western Extension of LCC. However, no evidence could be established for significant crash reduction in the second and third years after the removal. Findings should be indicative to the transport management policy that could improve the road safety in the Greater London in the long run.

Traffic flow prediction, one of the essential problems in traffic control and guidance systems, is still drawing increasing attention in recent years with the new methods tipped by the success of A...

Introduction In the near future, automated vehicles will drive on public roads together with traditional vehicles. Even though almost the whole academia agrees on that statement, the possible interferences between the two different kinds of driver are still to be analyzed and the real impacts on the traffic flow to be under-stood. Objectives Aim of this paper is to study one of the most likely L3 automated system to be deployed on public roads in the short term: Highway Chauffeur. The analysis of this system is carried out on a roadwork scenario to assess the positive impacts arising from a joint implementation of the automated system and the C-ITS Use Case signaling the closure of a lane. In fact, the main contribution of this paper is the assessment of the possible benefits in travel times and driving regime arising from the joint implementation of the Highway Chauffeur system and of C-ITS messages, both for the vehicles equipped with both technologies and for the surrounding traffic. Methods The assessment is achieved through traffic simulations carried out with the VISSIM software and a Python script developed by the authors. The overall process is described and the obtained results are provided, commented and compared to define the implementation of the C-ITS Use Case that could maximize the benefits of L3 driving. Results These results showed how triggering the take-over maneuver in ad-vance fosters the bottleneck efficiency (the same speed values reached between 80 and 100% Market Penetration for around 700 m range of the C-ITS message are reached at 50% Market Penetration with a 1500 m range). Besides, an in-creased speed up to 30 km/h at the bottleneck is recorded, depending on the mar-ket penetration and the message range. Finally, the delay upstream the roadworks entrance is reduced by 6% and arises at around 700 m, without the need to deploy the message up to 1500 m. Conclusions The paper investigates the impacts of take-over maneuvers and of automated driving while considering different operational parameters such as the message range. The results suggest all the potentialities of the Use Case while providing interesting figures that frame the trends related to the different imple-mentations. Finally, the tool developed to carry out the presented analysis is re-ported and made available so that hopefully the Use Case may be explored further and a precise impact assessment may be carried out with different prototypes of AVs and on different infrastructures.

Road traffic forecasting is crucial in Intelligent Transportation Systems (ITS). To achieve accurate results, it is necessary to model the dynamic nature and the complex non-linear dependencies governing traffic. The goal is particularly challenging when the prediction involves more than just one traffic variable. This paper proposes a novel multi-task learning model, called AST-MTL, to perform multi-horizon predictions of the traffic flow and speed at the road network scale. The strategy combines a multilayer fully-connected neural network (FNN) and a multi-head attention mechanism to learn related tasks while improving generalization performance. The model also includes the graph convolutional network (GCNs) and the gated recurrent unit network (GRUs) to extract the spatial and temporal features of traffic conditions. Our experiments employ new sets of GPS data, called OBU data, to perform traffic prediction in the freeway and urban contexts. The experimental results prove our model can effectively perform multi-horizon traffic forecasting for different types of roads and outperform state-of-the-art models., SCOPUS: ar.j, info:eu-repo/semantics/published

By designing road infrastructure, it is necessary to adapt the real situation to current development trends and respond accordingly to the intensity of traffic on the transport network. The development of the traffic situation is generally very dynamic, difficult to predict and influenced by a number of other factors. Modern technologies enable adaptive traffic flow management based on the sharing and evaluation of traffic information obtained in real time from traffic monitoring systems or even from vehicles as such (e.g. thanks to “Connected Vehicles” technology). The article first carries out a literature review of professional literature and scientific articles dealing with the issue of autonomous mobility and autonomous management of transport processes. That is followed by a description and creation of own algorithm for autonomous control of vehicles at the level crossing, including description of used data, methods and proposed solutions. Finally, the developed method (algorithm) is tested by the Anylogic simulation program in a real environment, as a case study of autonomous vehicle decision-making at the level crossing.

Advances in connected and autonomous vehicles have the promise to reshape the future of the transportation system. How and when the benefits associated with automation and connectivity technology will start to impact the performance of an urban corridor is an issue of interest for traffic operators. This paper proposes an analytical capacity model for urban corridors with mixed traffic based on the concept of macroscopic fundamental diagram. The model incorporates the full spectrum of connected and autonomous vehicle penetration rates as well as the reaction times of different vehicle following patterns. The connected and autonomous vehicle platoon intensity, formulated as an exponential function of the connected and autonomous vehicle penetration rate, is also considered in the proposed analytical capacity model. Numerical experiments are conducted to verify that different reaction time settings yield disparate results. Some reaction time settings were found to cause the corridor capacity to increase monotonically with the connected and autonomous vehicle penetration rate while others led to decreases in corridor capacity with connected and autonomous vehicle penetration rates. Finally, the validity of the proposed methodology is verified via simulation tests in VISSIM 2020.

Introduction:The violation of the Flight Information Region (FIR) over Indonesian airspace is one of the strategic issues related to the sovereignty of Indonesia.Methods:It requires a strategy for the alignment of the Indonesian FIR. One of the difficulties associated with the alignment process is managing the FIR, which is completely determined by the International Civil Aviation Organization (ICAO) owing to safety reasons.Results and Discussion:It is necessary to examine the preparedness and strategy of the Indonesian government to convince the ICAO and the international community that Indonesia is capable of controlling, managing, and securing Indonesian FIR airspace.Conclusion:This study proposes a roadmap for this FIR alignment strategy in an effort to maintain the integrity and sovereignty of the Indonesian region.

With the evolving demand for sustainable mobility, adequate charging infrastructure for electric vehicles (EVs) has been growing steadily and wireless power transfer (WPT) technology has been seen ...

Exploring traffic flow characteristics and predicting its variation patterns are the basis of Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on short-t...

The study aims at determining geo-reference locations for dynamic cooperative communications to be established, allowing the generation of dynamic vehicular gaps between the Dangerous Goods Vehicles and their surrounding vehicles, such that they could travel via a tunnel as a platoon and in isolation. This will ensure the safety of other road users in line with check-and-allow procedures at the road tunnel on Trans-European Transport Network, as per ADR regulations. The model is verified for different road layouts approaching a road tunnel in the UK, using varying traffic scenarios to determine if the identified geo-referenced location by the model is at suitable distances for gap generation. The results are compared against the simulated real-world tunnel traffic flow with conventional vehicles involving escorting of Dangerous Goods Vehicles via human operators. The mixed traffic and connected vehicles traffic flow scenarios are simulated for dynamic gap generation and compared against real-world tunnel scenario to analyse the improvements in travel time, queues and congestion.

Much of traffic flow theory and highway design is based upon the fundamental relationship, FR, between speed, flow, and density. Traditionally, the FR is assumed to be a static curve over time and space. This paper presents new empirical findings that show the FR in one lane of a freeway can actually change shape in a rapid and systematic response to conditions in the adjacent lanes. This paper demonstrates the dependencies for a general purpose freeway lane that is operating in the congested regime. The drivers become more conservative in response to slower speeds in either adjacent lane, conversely, they become less conservative in response to higher speeds in either adjacent lane. It is also shown that as drivers become more conservative the signals and waves move upstream slower. So this inter-lane dependency has a direct impact on how fast signals propagate through the traffic stream. For the study site the empirical relationship between the wave speed and relative speed is close to linear. This interdependence between lanes is an important factor that it is absent from most traffic flow and car following models. Given these findings the discussion explores hypothetical examples showing how the inter-lane dependencies can give rise to mechanisms for waves in adjacent lanes to synchronize, as well as a mechanism for a queue spreading from one lane to another without any lane change maneuvers. Finally, this work closes with an exploration of the possible physiological mechanisms that might be occurring in the drivers.

Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation.KeywordsTraffic flow Disturbance Time-varying OD Route choice Network disruption Dynamic travel time

The vision-based traffic flow parameter estimation is a challenging problem especially for dense traffic scenes, due to the difficulties of occlusion, small-size and dense traffic etc. Yet, previous methods mainly use detection and tracking methods to do vehicle counting in non-dense traffic scenes and few of them further estimate traffic flow parameters in dense traffic scenes. A framework is proposed to count vehicles and estimate traffic flow parameters in dense traffic scenes. First, a pyramid-YOLO network is proposed for detecting vehicles in dense scenes, which can effectively detect small-size and occluded vehicles. Second, the authors design a line of interest counting method based on restricted multi-tracking, which counts vehicles crossing a counting line at a certain time duration. The proposed tracking method tracks short-term vehicle trajectories near the counting line and analyses the trajectories, thus improving tracking and counting accuracy. Third, based on the detection and counting results, an estimation model is proposed to estimate traffic flow parameters of volume, speed and density. The evaluation experiments on the databases with dense traffic scenes show that the proposed framework can efficiently count vehicles and estimate traffic flow parameters with high accuracy and outperforms the representative estimation methods in comparison.

Present study reviews simulation models available for signalised and unsignalised intersections using cellular automata (CA). CA models are discrete dynamic systems, through which it is possible to model traffic behaviour with ease and simplicity. Thus, CA was chosen for the study. Driver, seepage behaviour and type of traffic (homogeneous/heterogeneous) affect the capacity and safety of the intersections. These models can be either with open boundary (with continuous deletion and generation of vehicles) or closed/periodic boundary (with a fixed number of vehicles). The proposed study also investigates about the boundary conditions of simulation models as these change the results of simulations. Further, frequent acceleration/deceleration activity at the intersection causes more emissions. It was found that most of the traffic simulation models developed for intersections lack in modelling interaction between vehicles, seepage, driver behaviour and emission behaviour etc. Very few studies were found to model the emission behaviour with CA. Present study chronologically lists studies with methods adopted, advancements in those methods, traffic behaviours taken into consideration and missing gaps between studies. The composition and interaction of five different modes such as cars, buses, trucks, motorised three-wheelers and motorised two-wheelers were considered as mixed traffic. A new methodology is suggested based on this review.

For many intelligent transportation applications, traffic congestion prediction is quite essential. If traffic congestion on the road ahead can be accurately and promptly predicted, and routes can be planned reasonably based on the prediction results, traffic congestion can be effectively alleviated. Aiming at the spatio-temporal correlation and evolution characteristics of traffic flow data, the Conv–BiLSTM module comprising a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) is proposed, considering the spatio-temporal features. Firstly, the obtained traffic speed data is folded according to spatio-temporal features, and a three-dimensional matrix is constructed as the input of the prediction network module. After the spatial features are extracted by the CNN, the temporal features and alignment features are extracted by the BiLSTM, followed by which the prediction results are obtained as an output. Prediction and evaluation experiments on the traffic data of the highway in Shanghai prove that the traffic congestion state predicted by this method is largely consistent with the actual state. The results demonstrate that the proposed method has a higher prediction accuracy compared with the conventional and state-of-the-art methods and is an efficient method of traffic congestion prediction.

In India, the majority of urban roads are undivided where the behavior of flows in a particular direction is predominantly influenced by the opposing traffic. Due to lack of lane segregation, the vehicles in ongoing direction occupy the opposing lane, which increases the lateral interactions between vehicles. These lateral interactions are influenced by various parameters such as vehicle types, driver behavior and vehicular speeds. Study of such complex interactions plays an important role in evaluating various management measures using microscopic simulation models. The lateral characteristics of vehicles, such as placement, separation and movement, act as necessary input for simulation models. The present study aims to analyze and model the lateral characteristics of vehicles on two-lane urban undivided roads. To achieve this, traffic flow data were collected from an urban undivided mid-block section in Bangalore City, India, using video graphic technique. Multiple linear regression model was developed for predicting the lateral placement of subject vehicle and it was found that lateral placement of subject vehicle is influenced by types and speeds of subject and opposing vehicles. Lateral separation for different types of ongoing (subject) and opposing pairs was also analyzed. The results show that both the ongoing and opposing vehicles have less freedom to move laterally when their sizes increase and hence, lateral separation decreases. The choice of path of vehicles' lateral shifts (left, current and right) on urban undivided roads was modeled using multinomial logistic regression. Lateral shift of a vehicle is influenced by speeds of subject vehicle and leader vehicle in current path, speed of leader vehicle in target path, and lateral gap between leader vehicles in current path and target path.

The increasing traffic congestion problem can be solved by an adaptive traffic signal control (ATSC) system as it utilises real-time traffic information to control traffic signals. Recently, deep reinforcement learning (DRL) has shown its potential in solving the traffic signal timing. However, one of the main challenges of DRL is to design a proper reward function and special attention needs for a multi-objective reward design. Since the feedback to the agent depends on the reward function, a proper design of reward function is needed for fast and stable learning. In this study, the authors proposed a new reward architecture called composite reward architecture (CRA) for multi-objective ATSC to optimise multiple objectives. It calculates multiple rewards in parallel for each action and applies the majority voting method to choose the desired action. Since the traffic signal of one intersection affects the adjacent intersections, a new coordination approach is proposed to get the overall smooth traffic flow. The proposed reward architecture CRA is compared with several existing reward functions used in the literature for different traffic scenarios. The new coordinated approach is compared with the non-coordinated approach. The authors demonstrated that the proposed approaches outperform the others concerning waiting time, halting the number of vehicles, and so on.

Real-time and accurate multi-lane traffic condition forecasting is of great importance to the connected and automated vehicle highway system. However, the majority of existing deep learning based traffic prediction methods focus on pursuing the precision of the methods while neglect to improve the efficiency of the methods. To achieve the high accuracy and high efficiency of multi-lane traffic flow prediction simultaneously, this study proposes a novel combination method via the integration of the clockwork recurrent neural network (CWRNN) and random forest (RF) method, which is RF-CWRNN. To the best of the authors’ knowledge, this is the first time that the CWRNN is introduced to capture the temporal feature of lane-level traffic flow and make traffic speed prediction. Meanwhile, the RF method is employed to measure the temporal relevance of the traffic flow and determine the optimal input time window. To verify the performance of the RF-CWRNN method, the ground-truth data of the expressways in Beijing were utilised to carry out experiments. The results indicate that the RF-CWRNN method is superior to the baseline models in terms of accuracy and robustness. Besides, the proposed method can save plenty of training time compared with the classical long short-term memory neural network.

Performing centrality analysis on nodes from transportation networks are critical to identify important hubs, understand travel decisions, and assess system performances. Current centrality measures are based on topological characteristics of nodes and edges. When applying those measures to large-scale transportation networks, two problems remain unsolved. First, measures are computed based on simplified travel paths, which only include origins and destinations. Due to the lack of information about waypoints of routes, such network representation may not preserve fine level information about waypoints, routes, and traffic flow patterns, resulting in an inaccurate view of centrality. Second, most centrality measures are global measures that rank all nodes in a network, thus failing to detect nodes of regional importance. Therefore, this paper describes an approach that leverages the concept of sequences to identify key waypoints from frequent travel paths and detect community structures of transportation networks. This approach extends two complementary centrality measures to define the role of nodes within communities. The approach has been tested using tracking data of ships in a regional maritime transportation network. Compared to traditional measurement approaches, the proposed approach can construct compact communities, discover prominent waypoints, and add new insight with local centrality measure.

Problem statement. To promote safe and comfortable driving operations, design manuals and policies call for merging terminal ramps to be built along straight roadway sections. However, this prescription may need to be overlooked when there is no alternative to terminals being designed along curves. This study aims to assess the impact of design factors on driver behaviour along curved acceleration terminals with continue or reverse curvature prior to joining the motorway. Methodology. A driving simulation experiment was conducted to observe longitudinal and transversal driver behaviour when certain factors (i.e., radius, ramp length, motorway curve direction, and traffic conditions along the motorway segment) were manipulated. The forty-eight drivers involved were separated into groups based on age and gender. Results. The motorway radius has a significant impact on longitudinal performance, while traffic volume impacts on the merging point where vehicles enter the adjoining motorway lane. Compared to linear terminals, the merging abscissa need to be longer on curved ones so as to compensate for the blind spot and enable drivers to identify and accept gaps between vehicles in the traffic flow. The ramp-terminal connection type influences the speed at the beginning of the terminal, the position of the merging point and transversal driver behaviour. Conclusions. Although some limitations in the study were necessary to limit their impact on the experimental design, the results here may prove useful when considering the adoption of appropriate engineering countermeasures at the design stage of entry terminals along curved motorway segments.

Understanding how traffic flows respond to fuel price changes is useful for traffic management. This study uses a dataset of 11.9 million hourly observations from 118 traffic count stations over 2010–2017 to investigate the relationship between gasoline prices and traffic flows in the state of New South Wales, Australia. The findings suggest that higher gasoline prices reduce traffic flows, with an average effect size of −0.04 in the hourly estimates. The elasticity is particularly pronounced during off-peak periods, both on weekdays (−0.10) and weekends (−0.07). In contrast, a positive effect of gasoline prices on traffic flows is observed for peak periods on weekdays (0.06). Evidence is also obtained that afternoon peak-hour speeds are faster when gasoline prices are higher, consistent with a lowering of traffic density. The research also finds a negative price elasticity of gasoline demand and that people are more likely to use public transport when gasoline prices are higher. The findings suggest that fuel excise plays a role in both reducing overall road dependence and alleviating the severity of some peak-hour traffic jams.

Efficient traffic survey analysis is a catalyst for achieving an efficient and advanced transport planning and sustainable urban development. Unfortunately, the obvious decline in the traffic survey on Nigerian urban roads has left the road transport system to be handicap of traffic data, quality service deliveries and characterized by traffic-related quagmires with devastating socio-economic consequences including congestion, crime and crash incidences. This paper examined traffic survey analysis on major highways in Ogun State, Nigeria using manual traffic count method for estimation of traffic volume and flow pattern. The traffic data gathered were presented and analyzed using descriptive and inferential (ANOVA and Student ‘t’ test) techniques to determine variations in vehicular traffic volume and flow situation (inbound and outbound). Findings revealed that Car/SUV is the most dominant traffic relative to others. Meanwhile, ANOVA results show significant variation in traffic volume on the selected highways (p. value < 0.05), while, the Student ‘t’ test shows no significant difference between traffic inbound and outbound. Findings also revealed that the physical condition of the highways is relatively poor. Based on these findings, the study recommended improvement measures and strategies to curtail traffic-related issues towards achieving efficient, safe, and quality road transport planning in Nigeria.

Road safety is one of the major concerns in the ever-growing traffic network. In addressing this, surrogate safety measures play a critical role in identifying collision instincts. Besides the added advantage of quantifying collision instincts in advance, surrogate safety measures have their limitations. For example, in some instances, those measures tend to show erroneous results. In this paper, a new surrogate safety measure Instant Heeding Time (IHT), is presented based on follower vehicle attention in the traffic streams. This new measure is integrated with a distance gap and the vehicles' speeds to assess probable rear-end collisions. Further, along with other safety measures, the developed safety framework is tested over a study section, with the help of trajectory datasets at three traffic flow conditions (free flow, capacity, and congested) under prevailing heterogeneous (mixed) traffic conditions. Based on the safety framework, it is observed that, in the case of free flow and capacity conditions, 23 and 55 probable rear-end collisions points are detected. At the congested conditions, no rear-end collision points are observed. Further, smaller vehicles in the traffic stream are associated with a higher number of rear-end collision instincts than other vehicle categories. The conceptualized safety framework can be applied on a real-time basis for monitoring the safety measures for vehicles in a mixed traffic stream.

The autonomous mobility-on-demand (AMoD) system plays an important role in the urban transportation system. The charging behavior of AMoD fleet becomes a critical link between charging system and transportation system. In this paper, we investigate a strategic charging pricing scheme for charging station operators (CSOs) based on a non-cooperative Stackelberg game framework. The Stackelberg equilibrium investigates the pricing competition among multiple CSOs, and explores the nexus between the CSOs and AMoD operator. In the proposed framework, the responsive behavior of AMoD operator (order-serving, repositioning, and charging) is formulated as a multi-commodity network flow model to solve an energy-aware traffic flow problem. Meanwhile, a soft actor-critic based multi-agent deep reinforcement learning algorithm is developed to solve the proposed equilibrium framework while considering privacy-conservation constraints among CSOs. A numerical case study with city-scale real-world data is used to validate the effectiveness of the proposed framework.

Realising the spatio-temporal evolutionary pattern of urban traffic can give advice about making personal trip route planning and improving road construction. A novel pattern-discovering model is presented to identify the traffic regularity and characteristics from spatial and temporal dimensions. To unveil this new method, there are two main parts as follows: first, by employing the constrained projected gradient of the non-negative matrix factorisation algorithm, the original traffic data matrix is decomposed into the feature matrix and the weight matrix. Necessary constraints are newly added so that the resulting matrices are ensured to make practical sense for reflecting the traffic spatio-temporal regular pattern. Then, the self-organising maps network is further used to cluster the factorisation error into several classes representing the disparate traffic pattern of each time. In addition, the experiment is conducted on real historical data to verify the performance of the algorithm. The global urban traffic flow for a week is summarised through a set of basic patterns with related weight distribution. The well-visualised result demonstrates that the authors method can achieve significant improvement in terms of computational efficiency and accuracy when compared with other widely-used methods.

Background The intersection traffic signal control problem (ITSCP) has become even more important as traffic congestion has been more intractable. The ITSCP seeks an efficient schedule for traffic signal settings at intersections with the goal of maximizing traffic flow while considering various factors such as real-time strategies, signal timing constraints, rapid developments in traffic systems, and practical implementation. Since the factors constituting the ITSCP exhibit stochastically complicated interactions, it is essential to identify these factors to propose solution methods that can address this complexity and still be practically implemented. Objective The objective of this review is to provide a survey of problems, methods, and practices in the evaluation of the ITSCP. In this paper, a unified terminology for the ITSCP and a citation network of the current body of relevant research are accordingly presented, and various assumptions, constraints, and solution approaches are summarized. A review across the entire body of knowledge throughout the history of the ITSCP is therefore provided. This review also highlights open issues and challenges that remain to be addressed by future research.

In this paper, we propose a two-stage robust optimization framework to evaluate widely controllable commuter flow strategies that can help mitigate uncertain disruption impacts on rail transit commuter flows. In the operation-as-usual stage, preemptive control strategies including timetable adjustment, commuter path choice diversion, and commuter trip starting time diversion are planned within budget and other requirement constraints. In the disruption stage, optimal contingency routing is derived to minimize disruption impacts. To this end, a linear programming model that simulates the commuter movement logic under train service and platform disruption events is developed, which is then integrated in a mixed-integer max-min optimization model to evaluate the worst-case impacts on commuter flows arising from a set of uncertain disruption scenarios. We show that the two-stage robust optimization framework can be solved by a cutting plane algorithm efficiently using high performance and parallel computing platforms. Finally, we demonstrate the application of the model in mitigating disruption impacts through computational studies based on an actual rail transit network.

The mixed flow of pedestrians and cyclists is frequently observed on the roads they share, but investigations of the dynamics of this kind of mixed flow have been very limited. This study proposes a heuristic-based model to reproduce the mixed-flow dynamics of pedestrians and cyclists, and the model is calibrated with an experiment on the mixed traffic flow of pedestrians and cyclists. Pedestrians/cyclists were asked to walk/ride on a ring-shaped track. In the uni/bidirectional flow scenario, pedestrians and cyclists moved in the same/opposite direction. A genetic algorithm was used for parameter calibration. The model could reproduce the experimental results well. Under both scenarios, pedestrians and cyclists formed their own lanes. The pedestrians walked in the inner lane, and cyclists rode in the outer lane in a self-organized process. The widths of the pedestrian lane and the cyclist lane were found to be more uniform during bidirectional flow. The pedestrian flow rate was higher in the unidirectional flow scenario than in the bidirectional flow scenario. In contrast, at low cyclist densities, the cyclist flow rate was essentially the same in both scenarios. When the density was high, the cyclist flow rate is higher in the unidirectional flow scenario. Sensitivity analyses showed that cyclist speed had little effect on the pedestrian flow rate. A higher cyclist speed led to a higher cyclist flow rate at low densities, but the cyclist flow rates approached the same value at high cyclist densities. As the proportion of pedestrians/cyclists increased, the flow rate of cyclists/pedestrians decreased. The simulation results on a straight track were largely consistent with those on a ring-shaped track.

Nearly 1.35 million people are killed annually on roads around the world and an additional 50 million are injured or disabled. Road traffic crashes are estimated to be the eighth leading cause of death globally for all age groups, the first among children and young people aged 5–29 years. These numbers highlight the urgency of the road safety issue for all governments and administrations. In their efforts to improve safety, road network managers can benefit from decision support tools able to assist them in monitoring and managing road safety interventions. This paper proposes a DEA-based decision support method to assist urban road safety management practitioners in identifying those roads where the needs to improve safety are the greatest. The method is applied to an Italian urban road network to define a hierarchy of hazardous road locations based on safety conditions. The social cost of accidents is used here for the first time as the only output indicator while the average number of conflict points at intersections and traffic flow are used as inputs. Both Constant Returns to Scale and Variable Returns to Scale DEA models, each oriented to input and output, are used. The comparison of the results makes it possible to identify the DEA model that seems best suited to be used as a decision support tool to advise urban road safety management by enabling a more careful definition of targeted priority lists of interventions.