Showing papers in "Transportmetrica in 2022"
TL;DR: In this paper , a Lagrangian relaxation-based solution approach is proposed to decompose the model into subproblems with respect to individual vehicles, and the results provide a number of insights that can help transit operators design cost-effective electric transit operational plans.
Abstract: ABSTRACT The planning and operational decision-making problems of electric transit systems have received significant attention recently in the process of transport electrification. Given an electrified electric transit system with constructed charging facilities, a coordinated bus charging scheduling strategy can improve the system's operating efficiency by fully utilising available charging resources. This paper proposes a novel optimisation approach for the electric bus charging scheduling problem. To tackle the nonlinear relationship between the amount of energy and the time spent charging, this paper discretizes the decision variables for the charging schedule into time intervals. A linear integer program is formulated with the objective of minimising the system's total charging time. A Lagrangian relaxation-based solution approach is proposed to decompose the model into subproblems with respect to individual vehicles. The results provide a number of insights that can help transit operators design cost-effective electric transit operational plans.
32 citations
TL;DR: In this paper , the authors proposed a set of enhancements to the Incremental Queue Accumulation (IQA) delay model to overcome the limitations of current models and proposed a hybrid signal performance measure that combines delay and arrival patterns to depict signal performance truthfully.
Abstract: Delay is one of the most important traffic signal performance measures. In coordinated networks, understanding the characteristics of vehicle arrivals is important for coordination purposes and to properly estimate delays. When observed on a cyclical basis in real-time, distinctive arrival patterns can lead to similar delays, which may go undetected by contemporary delay models. This study proposes a set of enhancements to the Incremental Queue Accumulation (IQA) delay model to overcome the limitations of current models. Additionally, this study proposes a hybrid signal performance measure that combines delay and arrival patterns to depict signal performance truthfully. The enhancements to IQA are realised through an algorithm for the identification of distinctive vehicle arrival groups based on high-resolution signal and detection data. The results demonstrate that the proposed model provides reliable delay estimates (MAPE score in range 4.3–11.2%) while reporting a number of traffic arrival characteristics that are not available from the benchmarked models.
8 citations
TL;DR: In this article , the authors used an innovative categorical data analysis method known as cluster correspondence analysis (CCA) to identify some critical clusters with a group of co-occurring variable categories.
Abstract: ABSTRACT Moped and seated motor scooter (50 ccs or less) riders have a relatively high risk of becoming crash casualties. Comparison between 2015 and 2019 fatal crash data indicates that fatal moped crashes have increased by 76%, whereas fatal motorcycle crashes have decreased by 2%. This study collected moped and seated motor scooter-related fatal crash data for five years (2015–2019) from the Fatality Analysis Reporting System (FARS) to perform the analysis. Using an innovative categorical data analysis method known as cluster correspondence analysis (CCA), this study identified some critical clusters with a group of co-occurring variable categories. The contextual understanding of fatal crash patterns could guide authorities in developing data-driven interventions and countermeasures aiming to minimize moped collisions and related fatalities. The findings of this study can provide a better understanding of the patterns of contributing factors in moped and seated motor scooter fatal crashes.
8 citations
TL;DR: In this paper , a comparative analysis among previously developed and two newly proposed parameterisations of the NB-L distribution, the negative binomial weighted Lindley (NB-WLindley) and NB-QL, was conducted.
Abstract: Several studies have reported the superior performance of the Negative Binomial–Lindley (NB-L) compared to the commonly used Negative Binomial distribution. Consequently, different parameterisations of the NB-L distribution have been introduced to further improve crash data modelling. However, little is known on how these models perform for different data domains. This study is documenting a comparative analysis among previously developed and two newly proposed parameterisations of the NB-L distribution, the negative binomial weighted Lindley (NB-WLindley) and the negative binomial quasi-Lindley (NB-QL). The results show that the NB-WLindley distribution performed better for the majority of data domains. Also, its generalised linear model (NB-WLindley GLM) showed superior statistical performance relative to the NB GLM and NB-L GLM. The results of this study contribute to the advancement of current predictive models used in transportation safety and provide insights for safety analysts and researchers when these models should be used.
8 citations
TL;DR: In this paper , the authors proposed a TLTW overtaking decision model using a level-k game theoretic framework, which can consider the mutual influences between the ego and oncoming vehicles of different driving styles.
Abstract: Overtaking on two-lane two-way (TLTW) highways is often associated with a high risk of crashing. However, existing models of TLTW overtaking decision, either mechanism- or learning-based, cannot handle well the dynamic coupling among the interacting drivers. For accurate overtaking modelling, it is crucial to consider the uncertainties of interacting vehicle behaviours, especially their driving styles. To address these needs, we propose a TLTW overtaking decision model using a level-k game theoretic framework, which can consider the mutual influences between the ego and oncoming vehicles of different driving styles. A dataset is built based on the TLTW overtaking experiments with two instrumented vehicles, then PCA and k-means clustering are used to classify three driving styles, i.e. aggressive, normal and conservative. By comparing the model predictions with the experiment data, the statistics and case studies show that the proposed model with driving style awareness can accurately describe driver decisions in TLTW overtaking.
6 citations
TL;DR: In this article , a memory-based deep reinforcement learning approach was used to adapt to human-driving habits, where the authors developed a personalised car-following model via a memorybased deep RL approach.
Abstract: To adapt to human-driving habits, this study develops a personalised car-following model via a memory-based deep reinforcement learning approach. Specifically, Twin Delayed Deep Deterministic Policy Gradients (TD3) is integrated with a long short-term memory (LSTM) (abbreviated as LSTM-TD3). Using the NGSIM dataset, unsupervised learning-based clustering and data feature analyses are performed. The driving characteristics related to safety, efficiency and comfort are extracted for different driving styles, i.e. aggressive, common and conservative. Then, reward functions are constructed for different driving styles by incorporating their driving characteristics. By resorting to the TD3 policy within a recurrent actor–critic framework, LSTM-TD3 optimises the car-following behaviour via trial-and-error interactions according to the reward functions. Results show that compared with LSTM-DDPG and DDPG, LSTM-TD3 reproduces personalised car-following behaviour with desirable convergence speed and reward. It reveals that LSTM-TD3 can reflect the essential difference in safety, efficiency and comfort requirements among different driving styles.
5 citations
TL;DR: In this paper , an improved Fog-related Intelligent Driver Model (FIDM) was developed to reproduce drivers' car-following behavior features by taking into account unobserved driver heterogeneity in fog condition.
Abstract: The paper aims to develop an improved Fog-related Intelligent Driver Model (FIDM) that reproduces drivers’ car-following behaviour features by taking into account unobserved driver heterogeneity in fog condition. A multi-user driving simulator experiment was performed, and a vehicle fleet consisting of nine vehicles was tested in different fog and speed limits conditions. The experimental results showed that the unobserved driver heterogeneity (the combination of intra-driver heterogeneity and inter-driver heterogeneity) tended to increase as the fog density decreased. The average following distance tended to increase with the decrease of fog density and increase of speed limit. Two indexes were proposed to verify the performance of the FIDM. The results showed that FIDM performed better in reproducing unobserved driver heterogeneity and average following distance compared to the current popular car-following models. This study contributes to an improved car-following model for better understanding traffic flow phenomena under foggy conditions.
5 citations
TL;DR: In this article , a novel LSTM-based deep neural network capable of simulating the different walking behaviours of individuals with and without disabilities was designed, which consists of three modules: the Disability module, the Environmental module and the Trajectory Prediction module.
Abstract: Pedestrian trajectory prediction is imperative in specific fields, such as crowd management and collision prevention in automated driving environments. In this study, a novel long-short-term memory (LSTM)-based deep neural network capable of simulating the different walking behaviours of individuals with and without disabilities was designed. This network consists of three modules: the Disability module, the Environmental module, and the Trajectory Prediction module. Data from a large-scale pedestrian walking behaviour experiment involving individuals with disabilities were used to train and test the network. These data correspond to several experiments. Each experiment attempts to capture the essence of individuals’ walking behaviour in different situations. By sequencing and normalising the input data and applying regularisation techniques, the network was successfully trained. The results were compared to state-of-the-art models, demonstrating that the network can predict pedestrians’ trajectories more accurately, especially when pedestrian heterogeneity is involved.
5 citations
TL;DR: In this paper , an innovative deep learning approach, Multi-Fused Residual Network (MF-ResNet) is proposed to forecast travel demand by converting the complex relevance among OD pairs into graphical-based spatial dependencies by treating OD matrix as the input of the model.
Abstract: ABSTRACT Short-term travel demand forecasting is the critical first step to support transportation system management. Complex relevance among Origin-Destination (OD) pairs, temporal dependencies, and external factors bring challenges to it. An innovative deep learning approach, Multi-Fused Residual Network (MF-ResNet) is proposed to forecast travel demand. The complex relevance among OD pairs is converted into graphical-based spatial dependencies by treating OD matrix as the input of the model. The residual network units enable MF-ResNet to model not only near but also distant spatial correlations. Three conv-based residual network units model the temporal closeness, mid-term periodicity, as well as long-term periodicity features, and Fully-Connected (F-C) layers capture external factors. The fusion techniques coordinate all of the features. The proposed method is applied to the short-term forecasts of metro OD matrix in Shenzhen, China. The experimental results show that MF-ResNet can capture multiple complex dependencies robustly and outperforms traditional methods in terms of forecasting accuracy.
5 citations
TL;DR: In this article , a dynamic route choice model for pedestrian with mixed crowds is developed, where pedestrian flow is regarded as a two-dimensional compressible continuum fluid and characteristic variables are described with mathematical functions.
Abstract: ABSTRACT This study develops a dynamic route choice model for pedestrian with mixed crowds. Pedestrian flow is regarded as a two-dimensional compressible continuum fluid. Then, characteristic variables are described with mathematical functions. Pedestrians are classified into two classes based on different route choice strategies: reactive and predictive dynamic user-optimal principles. Reactive pedestrians only consider the current information to choose the routes with the minimum instantaneous cost. Predictive pedestrians are assumed to know details about the future and choose routes with the minimum predictive actual travel cost. Two methods are used to solve the models. Numerical simulations are presented to demonstrate the effectiveness of the models and the algorithms. The numerical results show that the evacuation time of predictive pedestrians is shorter than that of reactive pedestrians. Moreover, for low-density situations, predictive pedestrians can help improve the overall evacuation efficiency. However, for high-density situations, predictive pedestrians fail to improve the efficiency.
5 citations
TL;DR: Wang et al. as mentioned in this paper investigated the factors that affect pedestrian-injury severities in crashes with familiar and unfamiliar drivers by employing mixed logit models and found that the factors of early morning and sunny weather condition will be better modelled as random parameters in the model for familiar drivers.
Abstract: Pedestrian injury in pedestrian-vehicle crash is significantly related to the driver, pedestrian, vehicle, crash and environment characteristics. Driver’s route familiarity has been found greatly associated with driving behaviours. Two-year pedestrian-vehicle crash data in Yunnan Province were studied to investigate the factors that affect pedestrian-injury severities in crashes with familiar and unfamiliar drivers by employing mixed logit models. Eight variables were found significant only in the familiar driver model. And six variables were found significant only in the unfamiliar driver model. Estimation findings indicate that the factors of early morning and sunny weather condition will be better modelled as random parameters in the model for familiar drivers and the same with the factors of rainy weather condition and afternoon peak in the model for unfamiliar drivers. Some more effective and targeted countermeasures are put forward for familiar drivers, unfamiliar drivers and transportation managers to reduce pedestrians’ injury severities.
TL;DR: Zhang et al. as discussed by the authors put forward a new game theory-based crowd evacuation model incorporating emotion contagion, where the influence of nearby pedestrians on an individual's decision-making process was modeled using game theory.
Abstract: Panic in an emergency can be highly contagious; this can cause a situation to rapidly spin out of control, with serious consequences. Prior studies on emotion contagion have focused on panic and have ignored the rationality of pedestrians. Considering both panic and calm, this paper puts forward a new game theory-based crowd evacuation model incorporating emotion contagion. Transitions between three emotional states, i.e. infected, sensitive, and unchangeable, reveal the contagion of personalized emotions in the crowd. The influence of nearby pedestrians on an individual’s decision-making process is modeled using game theory. The proposed methodology is here applied to a real-world subway evacuation problem in Beijing. Simulation results show that emotion contagion has an important effect on pedestrian evacuation time. Moreover, calm pedestrians were found to be very effective at calming the crowd in an emergency. If management personnel can go deep into a crowd to calm pedestrians, then panic will be rapidly reduced.
TL;DR: In this article , the authors proposed a hybrid Demand Responsive Connector (DRC) fed by shared bikes, which functions as an access/egress mode for certain request points.
Abstract: ABSTRACT Demand Responsive Connectors (DRCs) have become a more general-purpose flexible transit service that caters to patrons’ personal needs. The traditional DRC operation, however, suffers from low efficiency due to excessive detours and incurs diseconomies of scale with respect to the demand and service area. To tackle this issue, we propose a novel DRC fed by shared bikes, which functions as an access/egress mode for certain request points. Analytical models are derived for the joint design of such a hybrid system. A mixed-integer non-linear programme is established to minimise the total system cost. A heuristic solution algorithm is developed by combining the simulated annealing and branch-and-bound algorithms. A series of numerical cases are designed to evaluate the proposed system’s performance against the traditional ones. The results demonstrate that the introduction of shared bikes can reduce the DRC tour length and consequently save total system costs.
TL;DR: In this paper , a modified GA based on microscopic simulation was used to get the optimal speed limit combination for freeway lane drop traffic efficiency, which can guarantee the solution diversity and optimal results.
Abstract: ABSTRACT The primary objectives of this study were to use variable speed limits (VSL) upstream of freeway lane drop to maintain capacity and reduce congestion. As driving behaviours are the main reasons leading to capacity drop and the microscopic simulation can reflect driving behaviours precisely, microscopic simulations were first used to test lane drop scenarios. The objective function and constraints determined according to traffic engineering practice were optimised using a modified genetic algorithm (GA) based on microscopic simulation to get the optimal speed limit combination. The modified GA can guarantee the solution diversity and optimal results. Then, the cell transmission model, a macroscopic flow model, was used to crosscheck the simulated results. Both microscopic and macroscopic analysis results demonstrated that VSL could only improve lane drop traffic efficiency if speed limits were set appropriately. This study provided a new process from microscopic to macroscopic aspects for analysing traffic problems.
TL;DR: In this paper , a matched case-control approach was used to model car-following safety with both longitudinal and lateral driving characteristics. But, the authors did not address the confounding effects of unobserved driver heterogeneity.
Abstract: Car-following safety is related to both observed driving characteristics (e.g. car-following behaviour) and unobserved driver heterogeneity (e.g. drivers’ psychological features). Two major issues remain in the existing literature, i.e. limiting to longitudinal characteristics and not addressing the confounding effects of unobserved driver heterogeneity. This study takes a matched case–control approach to model car-following safety with both longitudinal and lateral driving characteristics. Unobserved driver heterogeneity is controlled by matching preceding and following vehicle IDs. Results show that unstable lateral movements of preceding vehicles and following vehicles contribute to higher crash risks. Comparison results on two datasets with different congestion levels reveal that it is safer in more congested traffic when the following vehicle maintains more stable longitudinal and lateral behaviours, and greater speed difference, headway, and spacing regarding its preceding vehicle. This study provides insights in enhancing roadway safety management and benefiting the automated vehicle development by warnings on associated risks.
TL;DR: In this article , the authors quantified the size of errors and the impacts on pedestrian destination choice models and provided guidelines on what level of error is acceptable given the scope of their research.
Abstract: Accurately calibrated pedestrian destination choice models help explain and predict foot traffic in public places by describing how individuals choose locations to visit. Model calibration relies on empirical data, which is subject to measurement errors that can obfuscate calibration. This contribution adds errors to simulated data in a controlled and realistic way which can be applied to many model specifications, demonstrated on a pedestrian destination choice model. Results show that errors can cause calibrated models to generate dynamics that differ substantially from the true dynamics, along with causing bias in parameters and decreased prediction accuracy. By quantifying the size of errors and the impacts on calibration, this work aims to guide researchers in pedestrian destination choice modelling on what level of error is acceptable given the scope of their research.
TL;DR: Wang et al. as mentioned in this paper proposed to build CAB lanes in urban transportation networks to promote the ridership of connected and autonomous buses (CABs), and the CAB lane allocation problem was formulated into a mixed-integer nonlinear program.
Abstract: ABSTRACT To promote the ridership of connected and autonomous buses (CABs), this paper proposes to build CAB lanes in urban transportation networks. Three travel modes are considered: CAB, connected and autonomous passenger vehicle (CAV) and human-driven passenger vehicle (HV). To better utilise the capacities of CAB lanes, CAVs are granted limited access while maintaining good levels of services on these lanes. Analyses show that deploying CAB lanes may substantially increase the ridership of CABs and increase total social benefit, and allowing limited CAV access can further improve the overall system performance. The CAB lane allocation problem is formulated into a mixed-integer nonlinear programme. Numerical tests are performed to demonstrate the approach.
TL;DR: In this article , the authors examined 1740 real-world motorway pull-out manoeuvres (pullout distance, speed differential with the leading vehicle, manoeuvre duration, pullout comfort zone) under different conditions and highlighted the significant impact of the surrounding traffic and the driving characteristics on or before the manoeuvre initiation point.
Abstract: Growing research attention is focusing on Automated Vehicle (AV) technologies, promising significant safety benefits. An in-depth understanding of human driving will play an important role in determining the most acceptable AV behaviour, supporting passenger comfort and thus the adoption of the technology, but also the optimal prediction of the behaviour of the surrounding traffic. The current study examined 1740 real-world motorway pull-out manoeuvres (pull-out distance, speed differential with the leading vehicle, manoeuvre duration, pull-out comfort zone) under different conditions. The results highlighted the significant impact of the surrounding traffic and the driving characteristics on or before the manoeuvre initiation point, which reflected the overtaking strategy selected. The findings can inform the design of automated overtaking systems that resemble human driving and thus encourage their uptake; in addition, they can assist the intention prediction for lane keeping assistance systems in order to optimise the system’s response to cutting in and pull-out manoeuvres.
TL;DR: In this paper , the authors derive revealed preferences from passively collected smart card data to analyse the role of waiting time reliability in public transport route choice, examining a number of indicators for the latter.
Abstract: ABSTRACT Unreliable waiting times may cause frustration and anxiety amongst public transport travellers. Although the effect of travel time reliability has been studied extensively, most studies have used stated preferences which have disadvantages, such as an inherent hypothetical bias, or have analysed revealed preferences for road traffic. Here, we derive revealed preferences from passively collected smart card data to analyse the role of waiting time reliability in public transport route choice. We study waiting time reliability as regular and irregular deviations from scheduled values, examining a number of indicators for the latter. Behaviour in morning peak and off-peak hours is contrasted and differences in reliability coefficients for different modes in the network, and for origin and transfer stops are reported. Results from The Hague indicate relatively low reliability ratios with travellers perceiving a 5-minute standard deviation in realised waiting times as an extra 1–5.6 min of planned waiting time.
TL;DR: In this paper , a series of unidirectional flow experiments are carried out to investigate the influence of a two-person social group with a strong relationship on pedestrian flow via changing corridor structures and components of the crowd.
Abstract: Investigating the pedestrian dynamics can help researchers and managers design pedestrian facilities more reasonably and develop crowd management plans scientifically. Researches show that most pedestrians in the crowd are a social group that has an impact on pedestrian movement characteristics in normal and emergency situations. In this study, a series of unidirectional-flow experiments are carried out to investigate the influence of a two-people social group with a strong relationship on pedestrian flow via changing corridor structures and components of the crowd. It is found that there are significant statistical differences between social groups and individuals at the micro-level, such as personal space. However, such differences are not reflected in the macro level, the presence of the social group has no significant impact on pedestrian dynamics, such as fundamental diagram and movement time. In addition, social groups adjust their relative positions and distance to alleviate the influence of high density.
TL;DR: Considering the fluctuations of daily container demand, an integer linear programming model is formulated to deal with the normal, congested, and insufficient demand scenarios in this paper , where the objectives correspond to the three scenarios are to minimize the total transportation time of all containers, the remaining containers, and the number of operating trains, respectively.
Abstract: ABSTRACT The passenger transportation-like organisation strategy is conducive to enhancing the market competitiveness of railway container transportation. However, many problems incurred by this strategy should be solved. This study focuses on the daily container-to-train assignment problem. Considering the fluctuations of daily container demand, an integer linear programming model is formulated to deal with the normal, congested, and insufficient demand scenarios. The objectives correspond to the three scenarios are to minimise the total transportation time of all containers, the number of remaining containers, and the number of operating trains, respectively. Finally, numerical experiments were conducted on a hypothetical railway line and the Lianyungang-Alataw pass railway corridor to demonstrate the application of the proposed approach.
TL;DR: In this paper , a study was conducted on the Transantiago public transport system of Santiago in Chile to understand how drivers approach the tools that seek to control the regularity of the headways.
Abstract: ABSTRACT The effectiveness of the control strategies applied in real-time to maintain regular headways between buses, especially those operating without schedules, requires the driver to execute the instruction from central control. This work aims to understand how drivers approach the tools that seek to control the regularity of the headways. Our study is conducted on the Transantiago, the public transport system of Santiago in Chile. Buses operating two routes of this service provider have been equipped with a headway control tool that provides drivers instructions to improve headway regularity. Our results show experienced drivers (over 50 years) perceive being faster and maintaining more regular headways than younger ones. They also appear to be more reluctant to use onboard headway control tools. Less experienced drivers recognize the accuracy of the information delivered and assure that it has improved their driving performance.
TL;DR: In this article , a series of experiments were conducted to study the movement of individuals through angled corridors with sharp-turn and curved-turn sections and the influence of different angled-corridors on the offset of trajectory, speeds, and the turning point is studied.
Abstract: Understanding the movement characteristics of pedestrians is essential for the management of mass gatherings. In this work, a series of experiments were conducted to study the movement of individuals through angled corridors with sharp-turn and curved-turn sections. The influence of different angled-corridors on the offset of trajectory, speeds, and the turning point is studied. An inward offset for pedestrians around the turning sections is observed regardless of movement speeds and directions. It is also found that the distance to the turning points (the relative difference between the selected reference points and turning points) in running is greater than that in normal walking. For clockwise and anticlockwise movements, there are no significant differences for different radii when walking. The results can be used as validation bases and behaviour rules for the microscopic pedestrian models.
TL;DR: In this article , the authors developed a conservative expected travel time approach with multiple cut points (MCET) for reporting reliable waiting time information to users in app-based transportation services, which is modelled as the expected value of a discretized travel time distribution according to cut points.
Abstract: Many app-based transportation services provide waiting time information to users before they book the services. Travel time is an important component of waiting time, and positive skew with a long upper tail is a basic characteristic of travel time variability. Waiting time information provision is expected to be reliable and easy to understand. However, the existing information provision forms involving travel time variability are either too optimistic in terms of having a high risk of delay or not easily understood by the users. This paper develops a conservative expected travel time approach with multiple cut points (MCET) for reporting reliable waiting time information to users in app-based transportation services. The MCET is modelled as the expected value of a discretized travel time distribution according to cut points. The numbers and locations of cut points are optimised by seeking a tradeoff between the attractiveness and credibility of the service platform.
TL;DR: In this paper , the Stochastic User Equilibrium (SUE) traffic assignment model is used for investigating the behaviors of travellers on congested road networks, where the functional forms in the correlation components are based upon generalised, flow-dependent congested costs, rather than e.g. length / free-flow travel time as done typically.
Abstract: The Stochastic User Equilibrium (SUE) traffic assignment model is a well-known approach for investigating the behaviours of travellers on congested road networks. SUE compensates for driver/modelling uncertainty of the route travel costs by supposing the costs include stochastic terms. Two key challenges for SUE modelling, however, are capturing route correlations and dealing with unrealistic routes. Numerous correlation-based SUE models have been proposed, but issues remain over both internal consistency and choice set robustness. This paper formulates (and proves solution existence for) new internally consistent SUE formulations of GEV structure and correction term logit route choice models, where the functional forms in the correlation components are based upon generalised, flow-dependent congested costs, rather than e.g. length / free-flow travel time as done typically. Numerical experiments are then conducted on the Sioux Falls and Winnipeg networks, where computational feasibility for obtaining internally consistent solutions, choice set robustness, and internal consistency are assessed/compared.