Showing papers in "Transportation Research Part C-emerging Technologies in 2017"

Deep learning for short-term traffic flow prediction

Abstract: We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers. The challenge of predicting traffic flows are the sharp nonlinearities due to transitions between free flow, breakdown, recovery and congestion. We show that deep learning architectures can capture these nonlinear spatio-temporal effects. The first layer identifies spatio-temporal relations among predictors and other layers model nonlinear relations. We illustrate our methodology on road sensor data from Interstate I-55 and predict traffic flows during two special events; a Chicago Bears football game and an extreme snowstorm event. Both cases have sharp traffic flow regime changes, occurring very suddenly, and we show how deep learning provides precise short term traffic flow predictions.

User preferences regarding autonomous vehicles

Abstract: This study gains insight into individual motivations for choosing to own and use autonomous vehicles and develops a model for autonomous vehicle long-term choice decisions. A stated preference questionnaire is distributed to 721 individuals living across Israel and North America. Based on the characteristics of their current commutes, individuals are presented with various scenarios and asked to choose the car they would use for their commute. A vehicle choice model which includes three options is estimated: (1) Continue to commute using a regular car that you have in your possession. (2) Buy and shift to commuting using a privately-owned autonomous vehicle (PAV). (3) Shift to using a shared-autonomous vehicle (SAV), from a fleet of on-demand cars for your commute. A factor analysis determined five relevant latent variables describing the individuals’ attitudes: technology interest, environmental concern, enjoy driving, public transit attitude, and pro-AV sentiments. The effects that the characteristics of the individual and the autonomous vehicle have on use and acceptance are quantified through random utility models including logit kernel model taking into account panel effects. Currently, large overall hesitations towards autonomous vehicle adoption exist, with 44% of choice decisions remaining regular vehicles. Early AV adopters will likely be young, students, more educated, and spend more time in vehicles. Even if the SAV service were to be completely free, only 75% of individuals would currently be willing to use SAVs. The study also found various differences regarding the preferences of individuals in Israel and North America, namely that Israelis are overall more likely to shift to autonomous vehicles. Methods to encourage SAV use include increasing the costs for regular cars as well as educating the public about the benefits of shared autonomous vehicles.

Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

Abstract: Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependencies, temporal dependencies, and exogenous dependencies need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependencies within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. The experimental results, validated on the real-world data provided by DiDi Chuxing, show that the FCL-Net achieves the better predictive performance than traditional approaches including both classical time-series prediction models and state-of-art machine learning algorithms (e.g., artificial neural network, XGBoost, LSTM and CNN). Furthermore, the consideration of exogenous variables in addition to the passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since they reduce the root mean squared error (RMSE) by 48.3%. It is also interesting to find that the feature selection reduces 24.4% in the training time and leads to only the 1.8% loss in the forecasting accuracy measured by RMSE in the proposed model. This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.

Free-floating bike sharing: Solving real-life large-scale static rebalancing problems

Abstract: Free-floating bike sharing (FFBS) is an innovative bike sharing model. FFBS saves on start-up cost, in comparison to station-based bike sharing (SBBS), by avoiding construction of expensive docking stations and kiosk machines. FFBS prevents bike theft and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. However, like SBBS, the success of FFBS depends on the efficiency of its rebalancing operations to serve the maximal demand as possible. Bicycle rebalancing refers to the reestablishment of the number of bikes at sites to desired quantities by using a fleet of vehicles transporting the bicycles. Static rebalancing for SBBS is a challenging combinatorial optimization problem. FFBS takes it a step further, with an increase in the scale of the problem. This article is the first effort in a series of studies of FFBS planning and management, tackling static rebalancing with single and multiple vehicles. We present a Novel Mixed Integer Linear Program for solving the Static Complete Rebalancing Problem. The proposed formulation, can not only handle single as well as multiple vehicles, but also allows for multiple visits to a node by the same vehicle. We present a hybrid nested large neighborhood search with variable neighborhood descent algorithm, which is both effective and efficient in solving static complete rebalancing problems for large-scale bike sharing programs. Computational experiments were carried out on the 1 Commodity Pickup and Delivery Traveling Salesman Problem (1-PDTSP) instances used previously in the literature and on three new sets of instances, two (one real-life and one general) based on Share-A-Bull Bikes (SABB) FFBS program recently launched at the Tampa campus of University of South Florida and the other based on Divvy SBBS in Chicago. Computational experiments on the 1-PDTSP instances demonstrate that the proposed algorithm outperforms a tabu search algorithm and is highly competitive with exact algorithms previously reported in the literature for solving static rebalancing problems in SBSS. Computational experiments on the SABB and Divvy instances, demonstrate that the proposed algorithm is able to deal with the increase in scale of the static rebalancing problem pertaining to both FFBS and SBBS, while deriving high-quality solutions in a reasonable amount of CPU time.

Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles

Abstract: Autonomous vehicles use sensing and communication technologies to navigate safely and efficiently with little or no input from the driver These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems In this paper we derive semiparametric estimates of the willingness to pay for automation We use data from a nationwide online panel of 1260 individuals who answered a vehicle-purchase discrete choice experiment focused on energy efficiency and autonomous features Several models were estimated with the choice microdata, including a conditional logit with deterministic consumer heterogeneity, a parametric random parameter logit, and a semiparametric random parameter logit We draw three key results from our analysis First, we find that the average household is willing to pay a significant amount for automation: about $3500 for partial automation and $4900 for full automation Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology Third, our semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology

Platoons of connected vehicles can double throughput in urban roads

Abstract: Intersections are the bottlenecks of the urban road system because an intersection’s capacity is only a fraction of the maximum flows that the roads connecting to the intersection can carry. This capacity can be increased if vehicles cross the intersections in platoons rather than one by one as they do today. Platoon formation is enabled by connected vehicle technology. This paper assesses the potential mobility benefits of platooning. It argues that saturation flow rates, and hence intersection capacity, can be doubled or tripled by platooning. The argument is supported by the analysis of three queuing models and by the simulation of a road network with 16 intersections and 73 links. The queuing analysis and the simulations reveal that a signalized network with fixed time control will support an increase in demand by a factor of (say) two or three if all saturation flows are increased by the same factor, with no change in the control. Furthermore, despite the increased demand vehicles will experience the same delay and travel time. The same scaling improvement is achieved when the fixed time control is replaced by the max pressure adaptive control. Part of the capacity increase can alternatively be used to reduce queue lengths and the associated queuing delay by decreasing the cycle time. Impediments to the control of connected vehicles to achieve platooning at intersections appear to be small.

Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment

Abstract: This research proposed an eco-driving system for an isolated signalized intersection under partially Connected and Automated Vehicles (CAV) environment. This system prioritizes mobility before improving fuel efficiency and optimizes the entire traffic flow by optimizing speed profiles of the connected and automated vehicles. The optimal control problem was solved using Pontryagin’s Minimum Principle. Simulation-based before and after evaluation of the proposed design was conducted. Fuel consumption benefits range from 2.02% to 58.01%. The CO2 emissions benefits range from 1.97% to 33.26%. Throughput benefits are up to 10.80%. The variations are caused by the market penetration rate of connected and automated vehicles and v/c ratio. No adverse effect is observed. Detailed investigation reveals that benefits are significant as long as there is CAV and they grow with CAV’s market penetration rate (MPR) until they level off at about 40% MPR. This indicates that the proposed eco-driving system can be implemented with a low market penetration rate of connected and automated vehicles and could be implemented in a near future. The investigation also reveals that the proposed eco-driving system is able to smooth out the shock wave caused by signal controls and is robust over the impedance from conventional vehicles and randomness of traffic. The proposed system is fast in computation and has great potential for real-time implementation.

How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China

Abstract: This paper aims to examine the impact of ridesourcing on the taxi industry and explore where, when and how taxis can compete more effectively. To this end a large taxi GPS trajectory data set collected in Shenzhen, China is mined and more than 2,700 taxis (or about 18% of all registered in the city) are tracked in a period of three years, from January 2013 to November 2015, when both e-hailing and ridesourcing were rapidly spreading in the city. The long sequence of GPS data points is first broken into separate “trips”, each corresponding to a unique passenger state, an origin/destination zone, and a starting/ending time. By examining the trip statistics, we found that: (1) the taxi industry in Shenzhen has experienced a significant loss in its ridership that can be indisputably credited to the competition from ridesourcing. Yet, the evidence is also strong that the shock was relatively short and that the loss of the taxi industry had begun to stabilize since the second half of 2015; (2) taxis are found to compete more effectively with ridesourcing in peak period (6–10 AM, 5–8 PM) and in areas with high population density. (3) e-hailing helps lift the capacity utilization rate of taxis. Yet, the gains are generally modest except for the off-peak period, and excessive competition can lead to severely under-utilized capacities; and (4) ridesourcing worsens congestion for taxis in the city, but the impact was relatively mild. We conclude that a dedicated service fleet with exclusive street-hailing access will continue to co-exist with ridesourcing and that regulations are needed to ensure this market operate properly.

A recurrent neural network based microscopic car following model to predict traffic oscillation

Abstract: This paper proposes a recurrent neural network based microscopic car following model that is able to accurately capture and predict traffic oscillation. Neural network models have gained increasing popularity in many fields and have been applied in modelling microscopic traffic flow dynamics due to their parameter-free and data-driven nature. We investigate the existing neural network based microscopic car following models, and find out that they are generally accurate in predicting traffic flow dynamics under normal traffic operational conditions. However, they do not maintain accuracy under conditions of traffic oscillation. To bridge this research gap, we first propose four neural network based models and evaluate their applicability to predict traffic oscillation. It is found that, with an appropriate structure and objective function, the recurrent neural network based model has the capability of perfectly re-establishing traffic oscillations and distinguish drivers characteristics. We further compare the proposed model with a classical car following model (Intelligent Driver Model). Based on our case study, the proposed model outperforms the classical car following model in predicting traffic oscillations with different driver characteristics.

Research and development of automatic train operation for railway transportation systems: A survey

Abstract: With the rapid development of communication, control and computer technologies in the last several decades, automatic train operation (ATO), for which the driver no longer has to cautiously operate the control handle, is emerging in many urban rail systems to replace traditional manual driving in recent years. As technology advances in railway systems, one theoretically challenging and practically significant problem is how to use the ATO system to make the current railway network more efficient with higher carrying capacity, lower cost and improved quality of service by optimized railway traffic management and train operation. In this review, we focus on this emerging technology of automatic train operation (ATO) for its theoretical development and practical implementations. Specifically, this study first presents the background of ATO technology in railways, which involves the detailed description of its development and implementation in urban metro systems, fundamental features and basic structure of a typical ATO system. Then, we present a comprehensive literature review in this area, in which the current studies are generally classified into three main aspects, i.e., train operation modeling techniques, train trajectory optimization and train speed control methods. Finally, the emerging requirements for current ATO systems and the most promising research directions in this area in the future are discussed explicitly, including (i) the practical implementation of ATO in main line and high-speed railways, (ii) the cooperative train operation methods for energy-saving issues and (iii) the integration of railway traffic control with advanced ATO technology.

Locating charging infrastructure for electric buses in Stockholm

Abstract: Charging infrastructure requirements are being largely debated in the context of urban energy planning for transport electrification. As electric vehicles are gaining momentum, the issue of locating and securing the availability, efficiency and effectiveness of charging infrastructure becomes a complex question that needs to be addressed. This paper presents the structure and application of a model developed for optimizing the distribution of charging infrastructure for electric buses in the urban context, and tests the model for the bus network of Stockholm. The major public bus transport hubs connecting to the train and subway system show the highest concentration of locations chosen by the model for charging station installation. The costs estimated are within an expected range when comparing to the annual bus public transport costs in Stockholm. The model could be adapted for various urban contexts to promptly assist in the transition to fossil-free bus transport. The total costs for the operation of a partially electrified bus system in both optimization cases considered (cost and energy) differ only marginally from the costs for a 100% biodiesel system. This indicates that lower fuel costs for electric buses can balance the high investment costs incurred in building charging infrastructure, while achieving a reduction of up to 51% in emissions and up to 34% in energy use in the bus fleet.

Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges

Abstract: In the past few years, the social science literature has shown significance attention to extracting information from social media to track and analyse human movements. In this paper the transportation aspect of social media is investigated and reviewed. A detailed discussion is provided about how social media data from different sources can be used to indirectly and with minimal cost extract travel attributes such as trip purpose, mode of transport, activity duration and destination choice, as well as land use variables such as home, job and school location and socio-demographic attributes including gender, age and income. The evolution of the field of transport and travel behaviour around applications of social media over the last few years is studied. Further, this paper presents results of a qualitative survey from travel demand modelling experts around the world on applicability of social media data for modelling daily travel behaviour. The result of the survey reveals positive view of the experts about usefulness of such data sources.

Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach

Abstract: In this paper, we present an ensemble learning approach for better understanding ridesplitting behavior of passengers of ridesourcing companies who provide prearranged and on-demand transportation services. An ensemble learning model is a weighted combination of multiple classification models or week classifiers to form a strong classification model. The goal of ensemble learning is to combine decisions or predictions of several base classifiers to improve prediction, generalizability, and robustness over a single classifier. This paper employs the Boosting ensemble by growing individual decision trees sequentially and then assembling these trees to produce a powerful classification model. To improve the prediction accuracy of ridesplitting choices, we explored real-world individual level data extracted from the on-demand ride service platform of DiDi in Hangzhou, China. Over one million trips of the four service types, i.e., Taxi Hailing Service, Express, Private Car Service, and Hitch, are explored with descriptive statistics. A variety of features that may impact ridesplitting behavior are ranked and selected by using the ReliefF algorithm, such as trip travel time, trip costs, trip length, waiting time fee, travel time reliability of origins/destinations and so on. The Boosting ensemble trees with full features and selected features are trained and validated using two independent datasets. This paper also verifies that ensemble learning is particularly useful and powerful in the ridesplitting analysis and outperforms three other widely used classifiers. This paper is one of the first quantitative studies that empirically reveal the real-world demand and supply pattern by exploring the city-wide data of an on-demand ride service platform.

Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities

Abstract: Accurately estimating driving styles is crucial to designing useful driver assistance systems and vehicle control systems for autonomous driving that match how people drive. This paper presents a novel way to identify driving style not in terms of the durations or frequencies of individual maneuver states, but rather the transition patterns between them to see how they are interrelated. Driving behavior in highway traffic was categorized into 12 maneuver states, based on which 144 (12 × 12) maneuver transition probabilities were obtained. A conditional likelihood maximization method was employed to extract typical maneuver transition patterns that could represent driving style strategies, from the 144 probabilities. Random forest algorithm was adopted to classify driving styles using the selected features. Results showed that transitions concerning five maneuver states – free driving, approaching, near following, constrained left and right lane changes – could be used to classify driving style reliably. Comparisons with traditional methods were presented and discussed in detail to show that transition probabilities between maneuvers were better at predicting driving style than traditional maneuver frequencies in behavioral analysis.

Estimating traffic volumes for signalized intersections using connected vehicle data

Abstract: Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT) . At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.

Discovering themes and trends in transportation research using topic modeling

Abstract: Transportation research is a key area in both science and engineering. In this paper, we present an empirical analysis of 17,163 articles published in 22 leading transportation journals from 1990 to 2015. We apply a latent Dirichlet allocation (LDA) model on article abstracts to infer 50 key topics. We show that those characterized topics are both representative and meaningful, mostly corresponding to established sub-fields in transportation research. These identified fields reveal a research landscape for transportation. Based on the results of LDA, we quantify the similarity of journals and countries/regions in terms of their aggregated topic distributions. By measuring the variation of topic distributions over time, we find some general research trends, such as topics on sustainability, travel behavior and non-motorized mobility are becoming increasingly popular over time. We also carry out this temporal analysis for each journal, observing a high degree of consistency for most journals. However, some interesting anomaly, such as special issues on particular topics, are detected from temporal variation as well. By quantifying the temporal trends at the country/region level, we find that countries/regions display clearly distinguishable patterns, suggesting that research communities in different regions tend to focus on different sub-fields. Our results could benefit different parties in the academic community—including researchers, journal editors and funding agencies—in terms of identifying promising research topics/projects, seeking for candidate journals for a submission, and realigning focus for journal development.

A data-driven optimization-based approach for siting and sizing of electric taxi charging stations

Abstract: This paper presents a data-driven optimization-based approach to allocate chargers for battery electric vehicle (BEV) taxis throughout a city with the objective of minimizing the infrastructure investment. To account for charging congestion, an M / M / x / s queueing model is adopted to estimate the probability of BEV taxis being charged at their dwell places. By means of regression and logarithmic transformation, the charger allocation problem is formulated as an integer linear program (ILP), which can be solved efficiently using Gurobi solver. The proposed method is applied using large-scale GPS trajectory data collected from the taxi fleet of Changsha, China. The key findings from the results include the following: (1) the dwell pattern of the taxi fleet determines the siting of charging stations; (2) by providing waiting spots, in addition to charging spots, the utilization of chargers increases and the number of required chargers at each site decreases; and (3) the tradeoff between installing more chargers versus providing more waiting spaces can be quantified by the cost ratio of chargers and parking spots.

Electric vehicle charging choices : modelling and implications for smart charging services

Abstract: The rollout of electric vehicles (EV) occurring in parallel with the decarbonisation of the power sector can bring uncontested environmental benefits, in terms of CO2 emission reduction and air quality. This roll out, however, poses challenges to power systems, as additional power demand is injected in context of increasingly volatile supply from renewable energy sources. Smart EV charging services can provide a solution to such challenges. The development of effective smart charging services requires evaluating pre-emptively EV drivers’ response. The current practice in the appraisal of smart charging strategies largely relies on simplistic or theoretical representation of drivers’ charging and travel behaviour. We propose a random utility model for joint EV drivers’ activity-travel scheduling and charging choices. Our model easily integrates in activity-based demand modelling systems for the analyses of integrated transport and energy systems. However, unlike previous charging behaviour models used in integrated transport and energy system analyses, our model empirically captures the behavioural nuances of tactical charging choices in smart grid context, using empirically estimated charging preferences. We present model estimation results that provide insights into the value placed by individuals on the main attributes of the charging choice and draw implications charging service providers.

Efficient control of fully automated connected vehicles at freeway merge segments

Abstract: This paper presents a longitudinal freeway merging control algorithm for maximizing the average travel speed of fully automated connected vehicles. Communication with a roadside unit allows the computation and transmission of optimized trajectories to the equipped vehicles. These vehicles then carry out the trajectories and resume normal operation once they cease communication with the roadside controller. A tool was developed to simulate and carry out the merging algorithm, while interfacing with the optimization software LINGO. A hypothetical merging segment was simulated to evaluate the effectiveness of the merging algorithm, and its performance is compared to conventional vehicle operation. During uncongested conditions the algorithm is able to reduce travel time, increase average travel speed and improve throughput. The capacity of the merge segment is directly related to the safe time gap selected to run the algorithm. Once capacity is reached, queuing forms on both the ramp and mainline segments upstream of the merge area. The algorithm provides safe merging operations during this congested traffic state.

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks ☆

Abstract: Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.

Empirically quantifying city-scale transportation system resilience to extreme events

Abstract: This article proposes a method to quantitatively measure the resilience of transportation systems using GPS data from probe vehicles such as taxis. The granularity of the GPS data necessary for the method is relatively coarse; it only requires coordinates for the beginning and end of trips, the metered distance, and the total travel time. The method works by computing the historical distribution of pace (normalized travel times) between various regions of a city and measuring the pace deviations during an unusual event. Periods of time containing extreme deviations are identified as events. The method is applied to a dataset of nearly 700 million taxi trips in New York City, which is used to analyze the city transportation infrastructure resilience to Hurricane Sandy. The analysis indicates that Hurricane Sandy impacted traffic conditions for more than five days, and caused a peak delay of two minutes per mile. Practically, it identifies that the evacuation announcements coincided with only minor disruptions, but significant delays were encountered during the post-disaster response period. Since the implementation of this method is very efficient, it could potentially be used as an online monitoring tool, representing a first step toward quantifying city scale resilience with coarse GPS data.

Development and validation of a questionnaire to assess pedestrian receptivity toward fully autonomous vehicles

Abstract: This study analyzes pedestrian receptivity toward fully autonomous vehicles (FAVs) by developing and validating a pedestrian receptivity questionnaire for FAVs (PRQF). The questionnaire included sixteen survey items based on attitude, social norms, trust, compatibility, and system effectiveness. 482 Participants from the United States (273 males and 209 females, age range: 18–71 years) responded to an online survey. A principal component analysis determined three subscales describing pedestrians’ receptivity toward FAVs: safety, interaction, and compatibility. This factor structure was verified by a confirmatory factor analysis and reliability of each subscale was confirmed (0.7

A study of pedestrian group behaviors in crowd evacuation based on an extended floor field cellular automaton model

Abstract: In the study of pedestrian movements, a consideration of group behaviors is important because of their potential impacts on pedestrian flow dynamics. In this paper, we investigate the group behaviors during emergency evacuation, which is a critical case for emergency crowd management but has not been fully explored and understood. It has been well recognized that in evacuation situations, some people within a crowd, especially those who are with families and friends, often move in small groups and act in particular patterns distinct from individuals. As a result, the crowd is a mixture of individuals and groups rather than a pure collection of individuals. To capture and evaluate the influence of group behaviors on crowd evacuation, we propose an extended floor field cellular automaton (CA) model that takes into account such phenomena. Our model is formulated by leveraging the leader-follower behavior rule that is evident in pedestrian group behaviors. To calibrate and validate the proposed model, a few field experiments of crowd evacuation were conducted in a university building. Through a representative case study, it is demonstrated that the proposed extended floor field CA model can replicate the well-known phenomena in crowd evacuation such as collective arch-like clogging at the exit as well as other commonly observed group behaviors in evacuation. Moreover, it is found that the total crowd evacuation time significantly increases with the presence of pedestrian groups in the crowd. The results also show that such negative effects of group behaviors in crowd evacuation intensify when the density of the crowd is higher. Subsequently, sensitivity analyses are performed to further explore how pedestrian group behaviors are influenced by model parameters that reflect the pedestrian flow dynamics in evacuation scenarios. With its capability of realistically replicating the field pedestrian evacuation, the proposed model can serve as a valuable tool for predicting crowd evacuation time and designing guidelines for pedestrian evacuation in emergency situations, in particular when group behaviors are salient.

Deployment of stationary and dynamic charging infrastructure for electric vehicles along traffic corridors

Abstract: As charging-while-driving (CWD) technology advances, charging lanes can be deployed in the near future to charge electric vehicles (EVs) while in motion. Since charging lanes will be costly to deploy, this paper investigates the deployment of two types of charging facilities, namely charging lanes and charging stations, along a long traffic corridor to explore the competitiveness of charging lanes. Given the charging infrastructure supply, i.e., the number of charging stations, the number of chargers installed at each station, the length of charging lanes, and the charging prices at charging stations and lanes, we analyze the charging-facility-choice equilibrium of EVs. We then discuss the optimal deployment of charging infrastructure considering either the public or private provision. In the former, a government agency builds and operates both charging lanes and stations to minimize social cost, while in the latter, charging lanes and stations are assumed to be built and operated by two competing private companies to maximize their own profits. Numerical experiments based on currently available empirical data suggest that charging lanes are competitive in both cases for attracting drivers and generating revenue.

Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation

Abstract: This driving simulator study, conducted as part of the EC-funded AdaptIVe project, assessed drivers’ visual attention distribution during automation and on approach to a critical event, and examined whether such attention changes following repeated exposure to an impending collision. Measures of drivers’ horizontal and vertical gaze dispersion during both conventional and automated (SAE Level 2) driving were compared on approach to such critical events. Using a between-participant design, 60 drivers (15 in each group) experienced automation with one of four screen manipulations: (1) no manipulation, (2) manipulation by light fog, (3) manipulation by heavy fog, and (4) manipulation by heavy fog with a secondary task, which were used to induce varying levels of engagement with the driving task. Results showed that, during automation, drivers’ horizontal gaze was generally more dispersed than that observed during manual driving. Drivers clearly looked around more when their view of the driving scene was completely blocked by an opaque screen in the heavy fog condition. By contrast, horizontal gaze dispersion was (unsurprisingly) more concentrated when drivers performed a visual secondary task, which was overlaid on the opaque screen. However, once the manipulations ceased and an uncertainty alert captured drivers’ attention towards an impending incident, a similar gaze pattern was found for all drivers, with no carry-over effects observed after the screen manipulations. Results showed that drivers’ understanding of the automated system increased as time progressed, and that scenarios that encourage driver gaze towards the road centre are more likely to increase situation awareness during high levels of automation.

Robust planning of dynamic wireless charging infrastructure for battery electric buses

Abstract: Battery electric buses with zero tailpipe emissions have great potential in improving environmental sustainability and livability of urban areas. However, the problems of high cost and limited range associated with on-board batteries have substantially limited the popularity of battery electric buses. The technology of dynamic wireless power transfer (DWPT), which provides bus operators with the ability to charge buses while in motion, may be able to effectively alleviate the drawbacks of electric buses. In this paper, we address the problem of simultaneously selecting the optimal location of the DWPT facilities and designing the optimal battery sizes of electric buses for a DWPT electric bus system. The problem is first constructed as a deterministic model in which the uncertainty of energy consumption and travel time of electric buses is ignored. The methodology of robust optimization (RO) is then adopted to address the uncertainty of energy consumption and travel time. The affinely adjustable robust counterpart (AARC) of the deterministic model is developed, and its equivalent tractable mathematical programming is derived. Both the deterministic model and the robust model are demonstrated with a real-world bus system. The results demonstrate that the proposed deterministic model can effectively determine the allocation of DWPT facilities and the battery sizes of electric buses for a DWPT electric bus system; and the robust model can further provide optimal designs that are robust against the uncertainty of energy consumption and travel time for electric buses.

The multi-objective railway timetable rescheduling problem

Abstract: Unexpected disruptions occur for many reasons in railway networks and cause delays, cancelations, and, eventually, passenger inconvenience. This research focuses on the railway timetable rescheduling problem from a macroscopic point of view in case of large disruptions. The originality of our approach is to integrate three objectives to generate a disposition timetable: the passenger satisfaction, the operational costs and the deviation from the undisrupted timetable. We formulate the problem as an Integer Linear Program that optimizes the first objective and includes e -constraints for the two other ones. By solving the problem for different values of e , the three-dimensional Pareto frontier can be explored to understand the trade-offs among the three objectives. The model includes measures such as canceling, delaying or rerouting the trains of the undisrupted timetable, as well as scheduling emergency trains. Furthermore, passenger flows are adapted dynamically to the new timetable. Computational experiments are performed on a realistic case study based on a heavily used part of the Dutch railway network. The model is able to find optimal solutions in reasonable computational times. The results provide evidence that adopting a demand-oriented approach for the management of disruptions not only is possible, but may lead to significant improvement in passenger satisfaction, associated with a low operational cost of the disposition timetable.

When human beings are like drunk robots: Driverless vehicles, ethics, and the future of transport

Abstract: It is often argued that driverless vehicles will save lives. In this paper, we treat the ethical case for driverless vehicles seriously and show that it has radical implications for the future of transport. After briefly discussing the current state of driverless vehicle technology, we suggest that systems that rely upon human supervision are likely to be dangerous when used by ordinary people in real-world driving conditions and are unlikely to satisfy the desires of consumers. We then argue that the invention of fully autonomous vehicles that pose a lower risk to third parties than human drivers will establish a compelling case against the moral permissibility of manual driving. As long as driverless vehicles aren’t safer than human drivers, it will be unethical to sell them. Once they are safer than human drivers when it comes to risks to 3rd parties, then it should be illegal to drive them: at that point human drivers will be the moral equivalent of drunk robots. We also describe two plausible mechanisms whereby this ethical argument may generate political pressure to have it reflected in legislation. Freeing people from the necessity of driving, though, will transform the relationship people have with their cars, which will in turn open up new possibilities for the transport uses of the automobile. The ethical challenge posed by driverless vehicles for transport policy is therefore to ensure that the most socially and environmentally beneficial of these possibilities is realised. We highlight several key policy choices that will determine how likely it is that this challenge will be met.

Analyzing year-to-year changes in public transport passenger behaviour using smart card data

Abstract: In recent years, there has been increased interest in using completely anonymized data from smart card collection systems to better understand the behavioural habits of public transport passengers. Such an understanding can benefit urban transport planners as well as urban modelling by providing simulation models with realistic mobility patterns of transit networks. In particular, the study of temporal activities has elicited substantial interest. In this regard, a number of methods have been developed in the literature for this type of analysis, most using clustering approaches. This paper presents a two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage. The strength of the proposed methodology is that it can model a continuous representation of time instead of having to employ discrete time bins. For each cluster, the approach provides typical temporal patterns that enable easy interpretation. The experiments are performed on five years of data collected by the Societe de transport de l’Outaouais. The results demonstrate the efficiency of the proposed approach in identifying a reduced set of passenger clusters linked to their fare types. A five-year longitudinal analysis also shows the relative stability of public transport usage.

Congestion-aware system optimal route choice for shared autonomous vehicles

Abstract: We study the shared autonomous vehicle (SAV) routing problem while considering congestion. SAVs essentially provide a dial-a-ride service to travelers, but the large number of vehicles involved (tens of thousands of SAVs to replace personal vehicles) results in SAV routing causing significant congestion. We combine the dial-a-ride service constraints with the linear program for system optimal dynamic traffic assignment, resulting in a congestion-aware formulation of the SAV routing problem. Traffic flow is modeled through the link transmission model, an approximate solution to the kinematic wave theory of traffic flow. SAVs interact with travelers at origins and destinations. Due to the large number of vehicles involved, we use a continuous approximation of flow to formulate a linear program. Optimal solutions demonstrate that peak hour demand is likely to have greater waiting and in-vehicle travel times than off-peak demand due to congestion. SAV travel times were only slightly greater than system optimal personal vehicle route choice. In addition, solutions can determine the optimal fleet size to minimize congestion or maximize service.