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Fifty years of evolution of transportation research is revisited based on bibliometric indicators of nearly 50,000 articles, the collective publication of all transportation journals. A multitude of objective indicators all consistently determined four major divisions in the field: (i) network analysis and traffic flow, (ii) economics of transportation and logistics, (iii) travel behaviour, and (iv) road safety. Trending themes of research within the abovementioned divisions respectively are: (i) macroscopic fundamental diagram and public transport network design, (ii) nil (no distinct trending topic), (iii) land-use, active transportation, residential self-selection, travel experience/satisfaction, social exclusion and transport/spatial equity, and (iv) statistical modelling of road accidents. Furthermore, clusters of research related to topics of (a) shared mobility, (b) electric mobility, and (c) autonomous mobility constitute trending topics that are each a cross between multiple divisions of the field. These outcomes document major directions to which the transportation research is headed. Additional outcome is determination of influential outsiders, seminal articles published by non-transportation journals that have proven instrumental in the development of transportation science. [ABSTRACT FROM AUTHOR]

We discuss a widely used air traffic flow management formulation. We show that this formulation can lead to a solution where air delays are assigned to flights during their take-off which is prohibited in practice. Although air delay is more expensive than ground delay, the model may assign air delay to a few flights during their take-off to save more on not having as much ground delay. We present a modified formulation and verify its functionality in avoiding incorrect solutions. [ABSTRACT FROM AUTHOR]

The increasing penetration of electric vehicles (EVs) results in growing interdependency between power and transportation networks. This paper proposes a hybrid economic-emission dispatch model to coordinate the operation of both networks toward social optima. Specifically, carbon emission from gasoline vehicles (GVs) is quantified by the macroscopic emission model, while the emission from EVs is indirectly characterized by that emitted from fossil power plants. To enhance regulation efficiency and improve social welfare, a differentiated pricing scheme is proposed to independently regulate GV traffic flow, EV traffic flow, and EV charging flow. Logarithmic transformation, combined with an accuracy-aware adaptive piecewise linearization (AAPWL) approximation method, is developed to reformulate the original nonlinear model into a tractable mixed-integer quadratic constrained programming. Numerical results from two test systems demonstrate the relationship between economic dispatch and emission dispatch. Meanwhile, the effectiveness and superiority of the proposed pricing scheme and the AAPWL method are validated. [ABSTRACT FROM AUTHOR]

With the development of transportation and the ever-improving of vehicular technology, Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS), especially in Traffic Flow Prediction (TFP). TFP plays an increasingly important role in alleviating traffic pressure caused by regional emergencies and coordinating resource allocation in advance to deployment decisions. However, existing research can hardly model the original intricate structural relationships of the transportation network (TN) due to the lack of in-depth consideration of the dynamic relevance of spatial, temporal, and periodic characteristics. Motivated by this and combined with deep learning (DL), we propose a novel framework entitled Fully Dynamic Self-Attention Spatio-Temporal Graph Networks (FDSA-STG) by improving the attention mechanism using Graph Attention Networks (GATs). In particular, to dynamically integrate the correlations of spatial dimension, time dimension, and periodic characteristics for highly-accurate prediction, we correspondingly devised three components including the spatial graph attention component (SGAT), the temporal graph attention component (TGAT), and the fusion layer. In this case, three groups of similar structures are designed to extract the flow characteristics of recent periodicity, daily periodicity, and weekly periodicity. Extensive evaluation results show the superiority of FDSA-STG from perspectives of prediction accuracy and prediction stability improvements, which also testifies high model adaptability to the dynamic characteristics of the actual observed traffic flow (TF). [ABSTRACT FROM AUTHOR]

Current traffic congestion and the resulting carbon emissions are two of the main problems threatening the sustainability of modern cities. The challenges facing today's cities focus primarily on the optimization of traffic flow and the transition to electric vehicles. The latter aspect implies the need for an adequate deployment of the infrastructure of charging stations. The inherent complexity in today's cities and the difficulty in implementing new policies whose benefits are difficult to measure and predict has led in recent years to consider the enormous potential of simulation tools and in particular of the agent-based simulation (ABS). ABS allows the specification of complex models that reflect the complexity and dynamism of urban mobility. Current technology in ABS has evolved and matured sufficiently to provide very sophisticated tools but lacking facilities for a flexible and realistic generation of input data in the execution of the experiments. In line with this, this paper introduces two configurable generators that automatize the creation of experiments in agent-based simulations. The generators have been developed with the SimFleet simulation tool enhancing the simulation of realistic movements and location of vehicles, passengers and other users of the urban traffic system within a city. The generators proved to be useful for comparing different distributions of locations as well as different agent movement behaviors based on real city data. [ABSTRACT FROM AUTHOR]

Despite their promise, popularity, and rapid growth, the transit implications of ride-hailing platforms (e.g., Uber, Lyft) are not altogether clear. On the one hand, ride-hailing services can provide pooling (i.e., traffic reductions) advantages by efficiently matching customer demand (i.e., trips) with resources (i.e., cars) or by facilitating car-sharing. On the other hand, ride-hailing may also induce extra travel because of increased convenience and travel mode substitution, which may create crowding (i.e., traffic increases). We seek to reconcile these divergent perspectives here, exploring the heterogeneous determinants of ride-hailing's effects. Taking advantage of Uber's staggered entry into various geographic markets in California, we execute a regression-based difference-in-differences analysis to estimate the impact of ride-hailing services on traffic volumes. Using monthly micro data from more than 9,000 vehicle detector station units deployed across California, we show that Uber's effect (either pooling or crowding) on traffic depends on various contextual factors. We find some evidence of pooling effects on weekdays; however, Uber's entry leads to significant crowding effects on weekends. Furthermore, the crowding effect is amplified on interior roads and in areas characterized by high population density. Although ride-hailing seems to have a substitution effect on public transportation, we find ride-hailing services may have a complementary effect for carpooling users. Finally, we show that premium ride-hailing services (e.g., Uber Black) almost exclusively lead to a crowding effect. We conduct a battery of robustness tests (e.g., propensity score matching, alternative treatment approaches, placebo tests) to ensure the consistency of our findings. [ABSTRACT FROM AUTHOR]

The traditional signalized intersection is no longer able to meet the increasing demands of transportation. A method combining autonomous intersection management (AIM) and connected and automated vehicles (CAVs) is suggested for solving existing traffic problems. This study proposes a control method for unsignalized intersections based on cooperative grouping, which discretizes the continuous traffic flow into a series of small-scale vehicle groups. In addition, a Petri Net model with a progressive structure is proposed, for which the time domain sequence of each group is strictly defined. To realize the global control of CAVs, the whole process of vehicle movement is decomposed into two processes: i) adjustment on branches and ii) coordination in the junction. All grouping conditions in the junction are clearly defined, and time windows are used for the discrete traffic flow modeling. Furthermore, a cooperative adaptive cruise control (CACC) model with bifurcations is proposed for grouping and following the control of vehicles on each branch. Simulation results show that vehicles are automatically grouped according to their initial positions, and no collision occurs during the whole process. Applied with the proposed method, the traffic capacity of a four-branch intersection exceeds 10000 veh/h under ideal conditions. The vehicles run more smoothly and show a better fuel economy than the other two methods. [ABSTRACT FROM AUTHOR]

At many airports, space on the apron is scarce and has to be used efficiently. To that end, we optimize the layout of aircraft parking positions adjacent to the airport terminal, pursuing two lexicographically ordered objectives. First, we minimize the number of aircraft that have to be diverted to remote parking positions, because positions adjacent to the terminal are not available. Second, we minimize the construction effort required for gate infrastructure. Aircraft collisions must be prevented at all times, and we consider various traffic situations, as traffic volume and fleet mix are not constant in time. We introduce the Airport Gate Layout Problem and formulate it as a mixed-integer model, which considers both greenfield and brownfield scenarios. To solve the problem efficiently, we introduce a decomposition framework that exploits the structure of the problem and employ various acceleration techniques. Our approach reduces computation times substantially, allowing us to solve instances that are intractable for CPLEX. Based on a case study for Munich Airport, we demonstrate how airports can gain valuable insights from solving the problem. • We present the Airport Gate Layout Problem and provide a mixed-integer formulation. • To solve the problem efficiently, we propose a decomposition-based approach. • Our approach solves large instances to optimality that are intractable for CPLEX. • We present a case study for a major European airport. [ABSTRACT FROM AUTHOR]

This paper proposes a framework of signalized corridor management with vehicle platooning and trajectory control (SCoPTO) using connected automated vehicle (CAV) technologies. In this framework, the major road platoons can request green time extensions when arriving at the downstream intersections to reduce unnecessary stops. The vehicular trajectories of CAVs can also be optimized to maximize the utilization of the green time and reduce the start-up lost time. CAV platooning operation is implemented such that CAVs can pass the intersection efficiently to potentially increase green time utilization and improve traffic stability. Simulations are performed in a realistic signalized corridor with three intersections, and the results show that the proposed SCoPTO framework can significantly improve the throughput and reduce the average delay across different CAV penetration rates. This study provides insights for traffic management centers to use SCoPTO for future urban signalized corridor management. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) and regular human-piloted vehicles (RVs) will coexist in the near future. Research has indicated the feasibility of using CAVs to stabilize traffic flow and eliminate stop-and-go waves. In light of this, we develop distributed control schemes to stabilize mixed vehicular platoons by using CAV as the leading vehicle. We address several challenges, such as limited information perception capabilities of RVs, the uncertain actuation of human drivers, and external disturbances. Only local information from the preceding vehicle is used in the controllers. Modifications of the input-to-state string stability (ISSS) are established to cope with the uncertainty and disturbance characteristics while conditions for stability are derived. The proposed control schemes can attenuate the effect of disturbances while maintaining the ISSS property. The relationships between ISSS and other string stability concepts are clarified. Finally, numerical experiments are conducted to validate the effectiveness of the proposed platoon control schemes. [ABSTRACT FROM AUTHOR]

When multiple equilibria exist, the desired traffic equilibrium, may not be reached through a day-to-day dynamic adjustment process. Han et al. ["Discrete-Time Day-to-Day Dynamic Congestion Pricing Scheme Considering Multiple Equilibria." Transportation Research Part B: Methodological 104: 1–16.] proposed a day-to-day dynamic pricing scheme charged on all links to ensure the desired traffic equilibrium can be achieved even when multiple equilibria exist. However, due to many practical constraints, the second-best pricing scheme has more practical interests. This study first discussed the issues of existing congestion pricing schemes for second-best case. Then, this study developed a second-best dynamic road pricing scheme that is implemented on a dynamic subset of the road links in the network. Our rigorous theoretical proof and numerical tests prove that the dynamic pricing scheme can drive the traffic state evolution towards the desired second-best traffic user equilibrium state from any initial traffic state. [ABSTRACT FROM AUTHOR]

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL – Data Driven Network Loading; DNL – Dynamic Network Loading; DTA – Dynamic Traffic Assignment; ITS – Intelligent Transportation Systems; OD – Origin Destination; TTF – Travel Time Function; LTT – Linear Travel Time; DL – Demand level [ABSTRACT FROM AUTHOR]

In this paper, pedestrian path prediction at a signalized crosswalk with pedestrian-electric bicycle-vehicle mixed flow is investigated. Firstly, a waiting/crossing decision model (W/CDM) is developed to predict pedestrians' waiting/crossing intentions with approaching vehicles. Secondly, a Modified Social Force Model (MSFM) is developed by taking the evasion with conflicting road users, the reaction to traffic lights and crosswalk boundary into consideration. The influence of pedestrians' heterogeneous characteristics and mixed traffic flow are considered for the first time. Then the mixed traffic data at a signalized crosswalk is recorded and analysed. A maximum likelihood estimation is proposed to calibrate the parameters. Finally, the integrated method (W/CDM-MSFM) is compared with the existing methods. The results show that the method outperforms the existing methods and accurately predicts the pedestrians' paths, which makes it possible for autonomous vehicles to present the feasibility to protect the safety of pedestrians and improve the efficiency of dynamic traffic. [ABSTRACT FROM AUTHOR]

Incorporation of Human Factors (HF) into the mathematical car-following (CF) models has always been the research hotspot. Ignorance of such inclusion would inevitably hinder us from acquiring a comprehensive understanding of traffic flow phenomena. This paper proposed a novel CF model in order to bridge three existing research gaps: the demand for more inclusion of HF into the Longitudinal Control Model (LCM); the requirement for a more desirable underlying CF model for the Task Capability Interface (TCI) model; the ignorance of the fuzziness of human brains when modeling Task Difficulty (TD). Specifically, in order to imitate driver's natural or subjective uncertainty and ambiguity of his TD, the fuzzy logic approach is introduced, and the TD is then incorporated into the LCM. Thereafter, both numerical simulation and field-data validation have been performed. Results indicate that our proposed model is more capable of accommodating HF and exhibits better performance than its predecessor. [ABSTRACT FROM AUTHOR]

This paper investigates the robust perimeter control for two urban regions with sampled-data and input saturation. Based on the macroscopic fundamental diagram, an uncertain network vehicle balance model is established for two urban regions. Instead of the continued feedback control, this paper designs a sampled-data feedback control law to stabilize the traffic accumulations at the desired state. Based on the Lyapunov function theory, the stability condition of two urban regions is given in the form of linear matrix inequalities (LMIs), which ensures that the traffic accumulation for each region is stabilized at the desired state. Moreover, for the inevitable disturbances, the robust saturation control strategy is designed such that the traffic accumulation for each region is robustly stabilized at the desired state, and meanwhile a less disturbance attenuation level is guaranteed. Numerical examples are given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Saturation flow estimation is a challenge in non-lane based mixed traffic streams due to the presence of mixed vehicle-types, each having different static and dynamic characteristics. Such a mix gives way to traffic behaviours like weak lane discipline, multiple-leader following, etc. Due to these behaviours, queue discharge flow during green time fluctuates, which makes it difficult to identify a prolonged steady period of queue discharge required to estimate saturation flow. The present study is based on the hypothesis that the Passenger Car Unit (PCU) can explain the fluctuations in discharge caused by mixed vehicle-types. Accordingly, this study proposes a new model to estimate saturation flow along with PCUs from field data. Mathematical properties of the model are studied before field applications. Field data collected from six different locations are used to estimate the saturation flow and PCUs. The results indicate that the model can capture complex interactions of various vehicle-types. [ABSTRACT FROM AUTHOR]

Trajectory data provide a rich source of information on spatial and temporal speed fluctuations of individual vehicles. To assemble a complete picture of traffic operations, obtaining vehicle trajectories of the entire traffic flow is essential. The current techniques cannot fulfill this need sufficiently. To this end, we developed a hybrid approach for reconstructing vehicle trajectories at signalized intersections that accounts for queue dynamics. Specifically, an extended variational solution network was established and Kalman Filtering was applied to capture and reproduce the stochastic properties of queue boundary curves. The proposed method was demonstrated using both NGSIM data and simulation data. Results showed that the hybrid approach reproduces accurately the shockwave boundaries and trajectory reconstruction. By fusing different sources of traffic data, the proposed method does not rely on the common assumption of uniform arrival between adjacent probe vehicles, which is the main drive for estimation improvement. [ABSTRACT FROM AUTHOR]

Patterns of traffic flow trajectories play an essential role in analysing traffic monitoring data in transportation studies. This research presents a data-adaptive clustering approach to explore traffic flow patterns and a unified algorithm to impute missing values for incomplete traffic flow trajectories. We recommend using subspace-projected functional data clustering with the assumption that each observed daily traffic flow trajectory is a realization of a random function sampled from a mixture of stochastic processes, and each subprocess represents a cluster subspace spanned by the mean function and eigenfunctions of the covariance kernel of the random trajectories. The unified algorithm combines probabilistic functional clustering with functional principal component analysis to propose a mixture prediction for missing value imputation. The proposed methods effectively unravel distinctive daily traffic flow patterns and improve the accuracy of missing value imputation. The advantage of the proposed approaches is demonstrated through numerical studies of a real traffic flow data application. [ABSTRACT FROM AUTHOR]

This paper investigates multi-period optimal energy scheduling and trading for multi-microgrids (MMGs) integrated with an urban transportation network (UTN). Specifically, an optimization based multi-period traffic assignment model is built to characterize the vehicular flows considering rational drivers with time-flexible travel demand. Meanwhile, each microgrid (MG) is modeled to independently schedule its operation by trading energy with other MGs and charging electric vehicles (EVs) with its fast charging stations (FCSs). The EV charging prices could further influence traffic flow distribution of the UTN. To co-optimize the coupled system, a multi-agent based optimal scheduling and trading scheme is proposed, which is facilitated by peer-to-peer communication of transactive energy information among the agents, thus can preserve privacy of the market participants. Within the scheme, the MGs and EVs take their optimal decisions in response to MG energy trading and EV charging prices updated via multi-bilateral negotiation based on the demand-supply relationship. When an equilibrium of the coupled system is reached, each MG can minimize its operation cost, and the traffic condition could also be improved. Case studies of a MMG system coupled with a double-ring UTN are conducted to validate the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]

The toll registers of Aschach in Upper Austria covering the period from 1627 to 1775 are an outstanding series of sources for the history of transport and trade in the early modern Upper Danube basin. At the Aschach toll station each vessel shipping up- and downstream the Danube as the most important traffic route in the Upper-German and Austrian area had to pay dues. The information comprised in the toll registers enables us to reconstruct the movement of goods and people between the Austrian Danube region and its neighbouring countries. This study analyzes the significances of the Upper Danube as a traffic route for Jews in the 1st half of the 18th century. Especially the Viennese Court Jews used the waterway for the supply of the Imperial army in Hungary with provisions and other military goods. They also received foodstuff, beverages and other consumer products via the Danube. Jewish traders, especially from Prague, used the Danube for their frequent journeys to the fairs in Linz, whereas other Jewish travelers on their way to Vienna or even further frequently embarked on vessels in Regensburg. [ABSTRACT FROM AUTHOR]

Platooning is an emerging transportation practice that has the potential to solve the problems of the burgeoning transportation industry. A platoon is a group of vehicles, with vehicle to vehicle communication, that travel closely behind one another such that the platoon can accelerate, brake and cruise together. Platoons can improve road safety, be energy efficient and reduce costs. Its complete socio-economic benefits include congestion mitigation, smoother traffic flow, better lane usage and throughput, incentives for green logistics and driver safety. The long-term effect of platooning on road transportation, if extensively deployed, would be better organised traffic flow and efficient tracking of vehicles on the road ushering a multilevel positive impact on the industry. In this study, we attempt to answer the critical question of whether platooning is an adoptable practice in the near future and discuss an agenda to take platooning closer to implementation on the ground by highlighting the opportunities for future research. We also present a conceptual framework to help researchers, academicians, policy makers and practitioners for the adoption of platooning into the transportation industry. [ABSTRACT FROM AUTHOR]

This paper puts forward a new methodology for time-variant seismic risk analysis of transportation networks. Risk here denotes the probability of exceeding the sum of direct loss due to repair costs of bridges in the network and indirect loss due to the reduced capacity for traffic flow. The proposed methodology employs reliability methods and a host of probabilistic models for earthquake events, damage, repair cost, downtime, and travel time, while accounting for time-varying phenomena of aging and discounting. A comprehensive application on the transportation network of Isfahan, Iran, is presented that features 64 bridges subject to seismicity from seven sources. [ABSTRACT FROM AUTHOR]

Accurate understanding and forecasting of traffic is a key contemporary problem for policymakers. Road networks are increasingly congested, yet traffic data is often expensive to obtain, making informed policy-making harder. This paper explores the extent to which traffic disruption can be estimated using features from the volunteered geographic information site OpenStreetMap (OSM). We use OSM features as predictors for linear regressions of counts of traffic disruptions and traffic volume at 6,500 points in the road network within 112 regions of Oxfordshire, UK. We show that more than half the variation in traffic volume and disruptions can be explained with OSM features alone, and use cross-validation and recursive feature elimination to evaluate the predictive power and importance of different land use categories. Finally, we show that using OSM's granular point of interest data allows for better predictions than the broader categories typically used in studies of transportation and land use. [ABSTRACT FROM AUTHOR]

The article discusses the possible impact of disasters on functionality of the transport critical infrastructure elements by overcoming their resilience. The aim of the article is to provide an appropriate approach to the resilience measurement through understanding of this close relationship. It was achieved by using combination of (1) a decisiontheoretical approach based on Influence Diagrams, which was used as a tool to model functionality disruption level of transportation network elements after disaster impact and (2) the time decomposition of the functionality disruption duration of these elements. Based on this approach, the transportation network element resilience assessment was conducted in form of the transportation element resilience loss. The proposed approach is intended to be applied to the critical infrastructure elements rather than to the transportation network as a whole. [ABSTRACT FROM AUTHOR]

The article presents a forecast of changes in the level of transport accessibility and mobility in Poland as a result of the anticipated development of the network of expressways and motorways. The progress which has been made in this respect in the last few years in Poland is unquestionable and unrepeatable by any other European country. Will the subsequent investment plans concerning the road network of the highest parameters offer equally impressive results as far as the increase in Poland's territorial cohesion is concerned? The aim of this article is to establish in what way the planned infrastructure investments will affect the changes in transport accessibility and mobility as well as whether they will result in the changes in traffic flows directed to Warsaw and other regional centres. To achieve this, an analysis of the present and target states of the road network in Poland was conducted from the perspective of the changes in accessibility, anticipated traffic flows, and mobility. For this purpose, the authors used the analyses of isochrone and accumulative accessibility in ArcMap environment and the research into traffic flows and their changes in the Visum software. The conducted research showed that the planned transport network might result in induced traffic through an increase in accessibility (the central variant) with the assumption that an increase in mobility would be vented in the real face of the phenomenon of motility. The fact of opening new road sections of expressways will contribute to substantial changes in the directions of traffic flows only to a slight extent, and the only transformations concern regions with already developed fast car transport infrastructures whose functionality is limited due to the lack of cohesion in the subsequent course or lack of a developed network of expressways and motorways. [ABSTRACT FROM AUTHOR]

We investigate the problem of controller design after linearization around the steady-state solutions of second-order traffic models. At steady-state the traffic variables follow space-dependent autonomous ODEs. The steady-state solutions can be used to account for the heterogeneity of the road in space, which can be caused by different environmental factors. Proceeding by analogy with open-channel flow studies, where such states are relevant for operating a network with efficient control strategies, we analyse and compare steady-state solutions for different second-order traffic models. We show that the steady-state density profile remains in three different regions. The behaviour of the considered models is discussed, leading to proposed corrections of the models. We select one corrected model, which we use for the design and implementation of a boundary controller to illustrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

South Korea is ranked as 4th among 34 nations of the Organization for Economic Cooperation and Development with 102 deaths in road accidents per one million population. This paper aims to investigate the factors associated with road accidents in South Korea. The rainfall data of the Korea Meteorological Administration and road accidents data of Traffic Accident Analysis System of Korea Road Traffic Authority is analyzed for this purpose. In this connection, multivariate regression analysis and ratio analysis with the descriptive analysis are performed to uncover the catastrophic factors involved. In turn, the results reveal that traffic volume is the leading factor in road accidents. The limited road extension of 1.47% compared to the 4.14% per annum growth of the vehicles is resulting in road accidents at such a large scale. The increasing proportion of passenger cars accelerate road accidents as well. 56% of accidents occur by the infringement of safety driving violations. The drivers with higher driving experience tend to have a higher accident ratio. The collected data is analyzed in terms of gender, driver experience, type of violations and accidents as well as the associated time of the accidents when they happen. The results indicate that 36.29% and 53.01% of accidents happen by male drivers in the day and night time, respectively. 29.15% of crashes happen due to safety infringement and violations of 41 to 60 years old drivers. The results demonstrate that population density is associated with the accidents frequency and lower density results in an increased number of accidents. The necessity of the state-of-the-art regulations to govern the urban road traffic is beyond dispute, and it becomes even more crucial for citizens’ relief since in our daily lives road accidents are getting more diverse. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to analyse recent trends in freight transport volumes as well as their relation to socio-economic and infrastructural variables, in the case of the following major European countries: France, Germany, Italy, Poland, Spain and the United Kingdom. This analysis refers to the period 2005-2016, so that years affected by the global economic crisis, which shows its peak in 2009, are taken into account. This research demonstrated that not all the countries under study show a strong relation between freight traffic and GDP as it could have been expected based on well consolidated experiences and studies. Moreover, other relations are investigated, with mixed results, between the freight traffic volumes and the extension of the rail and road networks as well as oil price data. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the effectiveness of two types of deceleration markings on freeways in China: fishbone-shaped (FS) markings and edge-rate (ER) markings. An actual 4-lane, 6-km long freeway in Province Hebei was created in a driving simulator and used for the experiments. Three scenarios of deceleration markings were adopted: one without markings, one with FS markings, and the third with ER markings. For each scenario, three traffic flow levels were adopted (low, medium, and heavy). The appropriate dimensions of deceleration markings were first established using relevant design guidelines and then used to develop the simulation scenarios using Multigen Creator software. Sixty drivers carried out the simulation experiments with eye tracker equipment. The adequacy of deceleration markings was analyzed with respect to speed, perceived distance, pupil diameter, and geometric parameters. The results showed that both types of deceleration markings made a certain effect on vehicle speed, drivers’visual behavior, and mental characteristics. However, the effect of the FS markings was more pronounced than that of the ER marking. Specifically, the FS markings showed a speed reduction of 12.3 km/h to 15.2 km/h and a perceived distance of 70 m to 90 m, compared with 6.7 km/h to 9.9 km/h and 40 m to 60 m, respectively, for the ER markings. Application comments of the results are provided in the conclusion. [ABSTRACT FROM AUTHOR]

Researchers have made significant strides in understanding car-following behaviour and traffic flow, especially with the advent of intelligent and networked technologies. Diverse mathematical models analyse traffic flow, each with pros and cons. This study focuses on a sensitivity-based mathematical model for uphill and downhill highways, examining position, velocity, and acceleration profiles to predict traffic jam occurrence. The model employs an ordinary differential equation and a machine learning-based approach (machine learning procedure neural network) for numerical solutions, exhibiting high accuracy (1 0 − 8 − 1 0 − 10) compared to the reference Runge–Kutta method For accuracy, reliability and stability of the results are evaluated by various performance indicators and statistical terms. For multiple independent executions, mean absolute deviation, root mean square error and error in Nash–Sutcliffe efficiency are calculated. Their values are lies in range 1 0 − 8 − 1 0 − 14 . Moreover, graphical analysis is established for better visualization of traffic flow and congestion. [ABSTRACT FROM AUTHOR]

Vehicle movements at signalized intersections should follow the guidance of lane marking arrows. Turns are permitted or banned depending on the existence of lane marking arrows establishing network link connectivity. Lane markings are the interface that joins consecutive upstream and downstream intersections. Traffic flows from origins to destinations across intersections should be governed by lane markings. In this study, conventionally fixed lane markings are relaxed as binary variables to be optimized by maximizing the green bandwidths. The proposed methodology is innovative in that it extends the lane-based design framework to incorporate green band maximization for enhancing traffic signal coordination. Path flows are controlled to satisfy flow conservations and to compile turning flows at intersections. With these turning flows as inputs, lane markings can be optimized together with the coordinated traffic signal settings. Path flows, path travel times, and path choices are evaluated through new linear constraints. For path travel times, cruise times along lanes and total delays at the ends of lanes are evaluated. The non-linear total delays are approximated by the proposed linearized delay function. The model coefficients are calibrated by network data as in a previous study. The problem is formulated as a binary-mixed-integer-linear-program and solved by standard branch-and-bound techniques using a CPLEX solver. To avoid non-linearity in the problem formulation, the bandwidth maximization approach is adopted instead of minimizing the total network delay in the design of the signalized network. A network with four intersections is provided to demonstrate how optimized lane markings can create efficient network link configurations. The numerical results are promising compared with those obtained in previous studies and show that the overall network performance can be improved. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms behind human mobility patterns is crucial to improve our ability to optimize and predict traffic flows. Two representative mobility models, i.e., radiation and gravity models, have been extensively compared to each other against various empirical data sets, while their fundamental relation is far from being fully understood. In order to study such a relation, we first model the heterogeneous population landscape by generating a fractal geometry of sites and then by assigning to each site a population independently drawn from a power-law distribution. Then the radiation model on this population landscape, which we call the radiation-on-landscape (RoL) model, is compared to the gravity model to derive the distance exponent in the gravity model in terms of the properties of the population landscape, which is confirmed by the numerical simulations. Consequently, we provide a possible explanation for the origin of the distance exponent in terms of the properties of the heterogeneous population landscape, enabling us to better understand mobility patterns constrained by the travel distance. [ABSTRACT FROM AUTHOR]

Abstract Lane changing and vehicular slowdowns are known to impact traffic flow. Using a modified Nagel–Schreckenberg cellular automata model for two vehicle types: blocking (e.g. cars) and non-blocking (e.g. motorcycles), we determined the thresholds at which the interplay of lane changing, random and non-random slowdowns strongly impact vehicle speeds. Lane changing improves speed with diminishing returns as vehicles opt to change lanes. At the same time, lane changing is detrimental to the overall speed when lane straddling occurs. Increasing random slowdowns beyond a critical value (in the case of motorcycles, slowdown values of p slow ≈ [ 0. 2 , 0. 3 , 0. 4 ] for densities ρ = [ 0. 20 , 0. 15 , 0. 10 ] respectively) can force crossover from free flowing traffic into a state where interactions between vehicles reduce the average speed. Highlights • A modified NaSch traffic model is proposed with the addition of lane changing. • The fraction of vehicles that change lanes move faster than those that do not. • Increasing the fraction of lane changers has diminishing returns in the speed difference of vehicles. • Phase transitions due to the value of slowdown probability are coupled with density. • Slowdown transition is different from the density phase transition. [ABSTRACT FROM AUTHOR]

The traditional U-turn design has significantly improved traffic operations for relieving traffic congestion. However, the U-turn diversion and merge segments still cause traffic conflicts and delays. In this paper, an exclusive spur dike U-turn lane (ESUL) is proposed with the aim of addressing the disadvantages of the traditional U-turn design. ESUL provides a separate U-turn lane to diverge, decelerate, U-turn, accelerate and merge without interacting with through traffic. The effectiveness of ESUL is demonstrated through a field data investigation, simulation and analysis with VISSIM software. The proposed design is evaluated in terms of three parameters: travel time, delay and number of stops. Compared to the traditional U-turn design, ESUL can reduce travel time by 29.15%, delay by 66.70% and the number of stops by 100% at most. The results showed that ESUL has better performance than the traditional U-turn design and could be implemented to reduce traffic congestion and the potential hazards caused by U-turn maneuvers. [ABSTRACT FROM AUTHOR]

Accurately forecasting the influence of disturbances in High-Speed Railways (HSR) has great significance for improving real-time train dispatching and operation management. In this paper, we show how to use historical train operation records to estimate the influence of high-speed train disturbances (HSTD), including the number of affected trains (NAT) and total delayed time (TDT), considering the timetable and disturbance characteristics. We first extracted data about the disturbances and their affected train groups from historical train operation records of Wuhan-Guangzhou (W-G) HSR in China. Then, in order to recognize the concatenations and differences of disturbances, we used a K-Means clustering algorithm to classify them into four categories. Next, parametric and nonparametric density estimation approaches were applied to fit the distributions of NAT and TDT of each clustered category, and the goodness-of-fit testing results showed that Log-normal and Gamma distribution probability densities are the best functions to approximate the distribution of NAT and TDT of different disturbance clusters. Specifically, the validation results show that the proposed models accurately revealed the characteristics of HSTD and that these models can be used in real-time dispatch to predict the NAT and TDT, once the basic features of disturbances are known. [ABSTRACT FROM AUTHOR]

Due to their simplicity and operating mode, magnetic loops are one of the most used traffic sensors in Intelligent Transportation Systems (ITS). However, at this moment, their potential is not being fully exploited, as neither the speed nor the length of the vehicles can be surely ascertained with the use of a single magnetic loop. In this way, nowadays the vast majority of them are only being used to measure traffic flow and count vehicles on urban and interurban roads. This is the reason why we presented in a previous paper the double magnetic loop, capable of improving the features and functionalities of the conventional single loop without increasing the cost or introducing additional complexity. In that paper, it was introduced their design and peculiarities, how to calculate their magnetic field and three different methods to calculate their inductance. Therefore, with the purpose of improving the existing infrastructure and providing it with greater potential and reliability, this paper will focus on justifying and demonstrating the advantages offered by these double loops versus the conventional ones. This will involve analyzing the magnetic profiles generated by the passage of vehicles over double loops and comparing them with those already known. Moreover, it will be shown how the vehicle speed, the traffic direction and many other data can be obtained more easily and with less margin of error by using these new inductance signatures. [ABSTRACT FROM AUTHOR]

Highlights • The paper analyses the implications of data aggregation on freight trip generation. • Constant estimations (on activity and activity-workforce categories) are provided. • Functional form models on activity categories are also assessed. • Functional form models are in general more accurate than only constant FTG rates. • The choice of the functional form is more relevant than that of the categorization. • Results have important impacts in data collection and FTG estimation in practice. Abstract This paper analyzes the impacts of aggregation level and category construction on the relevance and quality of freight trip generation (FTG) models. More precisely, constant generations and functional form models are compared, as well as activity and activity-workforce categories. The paper proposes a method to compare constant generation and functional form models on different category classifications based on MAPE estimations. Functional forms are assessed via linear regression and compared using Pearson coefficient. Results show that the aggregation level has not always a positive impact on the model's accuracy and the choice of suitable functional form leads to more accurate models. [ABSTRACT FROM AUTHOR]

Highlights • Introduction of machine learning techniques to freight transport modeling. • Bayesian classifier used for large-scale operationalization of latent segments. • Moderate and reasonable sensitiveness to a measure which induces tariff variations. • Behavioral heterogeneity captured by latent segments based on logistics requirements. Abstract This paper elaborates a novel approach for implementation of latent segments concerning behaviorally sensitive shipment size choice in strategic interregional freight transport models. Discrete shipment size choice models are estimated for different homogenous segments formed by latent class analysis. A machine learning technique called Bayesian classifier is applied to link segments obtained from a sample to data of commodity flows being available on a national level. Finally, in an exemplary scenario, the impact of information and communication technologies on shipment size distributions is calculated, revealing moderate elasticities and a predominant substitution of less than truck loads by full truck loads. [ABSTRACT FROM AUTHOR]

Highlights • Travel pattern of trips to/from collection-delivery points (CDPs) are studied. • Disaggregated collection-delivery trip demand model is developed. • Substantial VKT reduction can be achieved from relocating CDPs to suburban/rural areas. Abstract The transport impacts of collection-delivery points (CDPs), as an alternative to home delivery, are rarely studied. As e-shopping becomes increasingly popular, trips to collect deliveries at CDP, especially by car travel, may generate a considerable amount of external effects, such as emissions. Therefore, this paper analysed the "picking up/leaving goods" trips selected from the Swedish National Travel Survey and jointly modelled the individuals' mode choice and trip chaining decisions using a panel cross-nested logit model. The roles of trip chain characteristics, individual socio-demographics and land use characteristics on each trip chain and mode choice combination are investigated. The results indicate observed and unobserved heterogeneities of trip chaining and mode choice decisions among populations. Young adults living with partners/spouses, single adults with children and partnered adults with children have the preference of using cars in collection-delivery trips compared to other life-cycle groups. A sensitivity analysis is carried out to estimate the effect of distance to CDPs on vehicle kilometres travelled. The calibrated model is used to estimate the VKT of collection-delivery trips in the greater Stockholm area. The results indicate a 22.5% reduction of VKT from collection-delivery trips by relocating 5% CDPs from urban areas to suburban and rural areas. [ABSTRACT FROM AUTHOR]

Highlights • A multi-scale multi-regional input–output (MSMRIO) model is made dynamic. • It is applied to the German food supply system and based on comprehensive data. • The modelled spatial interactions are calibrated on official data. • It determines the additionally needed transport capacity in case of a disruption. • The transparency of supply system is increased and vulnerabilities uncovered. Abstract This paper presents a calibrated dynamic multi-scale multi-regional input–output (MSMRIO) model of the German food supply system based on real data. The model comprises 51 commodity groups from farm to fork differentiating three different temperature ranges as well as living animals. Spatially, it works on an aggregate level of 402 regions within Germany as well as its 50 most important trading nations. It determines the commodity flows and the additionally needed transport capacity in case of disruptions. Showing how changes in production, inventories, sourcing, and consumption affect commodity flows, the model uncovers vulnerabilities and makes risk evaluation possible. [ABSTRACT FROM AUTHOR]

Highlights • Conversion into a route choice problem enables consideration of traffic variability. • Choice probability model can easily capture effects of non-recurring traffic events. • The model calibrated by massive trip data identifies trip anomalies dynamically. • Unintentional trip anomalies can be separated from the intentional ones effectively. Abstract Taxi service is an essential complement to public transport systems due to its convenience and availability. It often provides hundreds of millions of rides for urban travelers every year in cities across the world. At the same time, the number of trip-induced passenger complaints about trip anomalies (trips with anomalous trip length, time, fare, etc.) is also considerable. Hence, the taxi regulators impose harsh penalties on verified trip anomalies. The existing anomaly verification process is labor-intensive, and it does not consider the traffic variability as well as the passengers' perception of trip anomalies. Quite often the imprecise and unfair outputs are generated as a result. To tackle this issue, we propose a choice probability-based taxi trip anomaly detection model (ProbDetect) that considers the taxi drivers' route choice behavior as well as the traffic variability. We first generate a route choice set for each OD pair based on the massive taxi GPS trajectory data. Second, we assign each route with a choice probability derived from a cumulative multivariate probability over differences of generalized costs. Third, we distinguish the unintentional anomalies from the intentional anomalies using the expected and the realized choice probability. Lastly, the model is tested on 5000 OD pairs using 180 days of taxi GPS data in Shanghai, China. Three types of anomalies are detected as a result. Insights into the driver's route choice behavior are derived as well. [ABSTRACT FROM AUTHOR]

Highlights • Multiple congestion pricing and tolling strategies are investigated. • Different scenarios are considered for an AV-oriented world and an SAV-oriented world. • An agent-based model is adopted for simulations of the Austin, Texas region. • Effects of policies on traffic conditions and social welfare are measured. • More complex and advanced strategies perform better, depending on the studied scenario. Abstract The introduction of autonomous (self-driving) and shared autonomous vehicles (AVs and SAVs) will affect travel destinations and distances, mode choice, and congestion. From a traffic perspective, although some congestion reduction may be achieved (thanks to fewer crashes and tighter headways), car-trip frequencies and vehicle miles traveled (VMT) are likely to rise significantly, reducing the benefits of driverless vehicles. Congestion pricing (CP) and road tolls are key tools for moderating demand and incentivizing more socially and environmentally optimal travel choices. This work develops multiple CP and tolling strategies in alternative future scenarios, and investigates their effects on the Austin, Texas network conditions and traveler welfare, using the agent-based simulation model MATSim. Results suggest that, while all pricing strategies reduce congestion, their social welfare impacts differ in meaningful ways. More complex and advanced strategies perform better in terms of traffic conditions and traveler welfare, depending on the development of the mobility landscape of autonomous driving. The possibility to refund users by reinvesting toll revenues as traveler budgets plays a salient role in the overall efficiency of each CP strategy as well as in the public acceptability. [ABSTRACT FROM AUTHOR]

Highlights • Model heterogeneous flows in the interdependent infrastructure system. • Consider partial capacity losses under facility disruption risks. • Formulate a non-linear integer programming model for the studied problem. • Draw insights into the features of the proposed model with numerical examples. • Reveal managerial insights into optimal location design and facility improvement. Abstract Due to the interdependency between multiple infrastructure systems, the performance of a facility may depend on the resources or supplies received from other facilities. However, cross-system interdependence has seldom been studied in the location design context, probably due to the lack of a concise model describing interdependence across heterogeneous systems. This paper proposes a new heterogeneous flow scheme to describe cross-system interdependence. This scheme has two features distinguished from existing models in describing an interdependent facility location problem. First, it is a simple linear model upon which a compact facility location model can be built. Secondly, it relaxes the need to maintain flow conservation between different systems and is suitable in describing heterogeneous systems that take in and output different resources or services. Built on this scheme, this paper proposes a reliable location design model for a nexus of interdependent infrastructure systems. This model aims to locate the optimal facility locations in multiple heterogeneous systems to balance the tradeoff between the facility investment and the expected nexus operation performance. Different from other reliable facility location models, this expected performance captures interdependence among heterogeneous systems due to the resource input-output relationships. The consideration of continuous partial capacity losses complements the reliable location literature that mainly focuses on binary disruptions. Two numerical examples are conducted for investigating features and applications of the proposed model. The results indicate that with a standard off-the-shelf integer programming solver, the proposed model is able to solve optimal facility location design for problem instances of realistic scales to the near-optimum solutions with optimality gap assurance. Sensitivity analyses of key parameters indicate that improving facility capacity and reducing interdependency between systems can mitigate impacts of facility capacity losses and reduce the overall system cost. [ABSTRACT FROM AUTHOR]