Role of street patterns in zone-based traffic safety analysis

Although extensive analyses of road segments and intersections located in urban road networks have examined the role of many factors that contribute to the frequency and severity of crashes, the explicit relationship between street pattern characteristics and traffic safety remains underexplored. Based on a zone-based Hong Kong database, the Space Syntax was used to quantify the topological characteristics of street patterns and investigate the role of street patterns and zone-related factors in zone-based traffic safety analysis. A joint probability model was adopted to analyze crash frequency and severity in an integrated modeling framework and the maximum likelihood estimation method was used to estimate the parameters. In addition to the characteristics of street patterns, speed, road geometry, land-use patterns, and temporal factors were considered. The vehicle hours was also included as an exposure proxy in the model to make crash frequency predictions. The results indicate that the joint probability model can reveal the relationship between zone-based traffic safety and various other factors, and that street pattern characteristics play an important role in crash frequency prediction.

A modification of local path marginal cost on the dynamic traffic network

Path marginal cost （PMC） is the change in totaltravel cost for flow on the network that arises when timedependentpath flow changes by 1 unit. Because it is hardto obtain the marginal cost on all the links, the local PMC,considering marginal cost of partial links, is normallycalculated to approximate the global PMC. When analyzingthe marginal cost at a congested diverge intersection, ajump-point phenomenon may occur. It manifests as alikelihood that a vehicle may unsteadily lift up （down） inthe cumulative flow curve of the downstream links. Previously,the jump-point caused delay was ignored whencalculating the local PMC. This article proposes an analyticalmethod to solve this delay which can contribute toobtaining a more accurate local PMC. Next to that, we usea simple case to calculate the previously local PMC and themodified one. The test shows a large gap between them,which means that this delay should not be omitted in thelocal PMC calculation.

Traffic Signal Timing via Deep Reinforcement Learning

In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed. © 2014 Chinese Association of Automation.

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

This study aims to explore the role of spatial heterogeneity in ridership analysis and examine the relationship between built environment, station attributes and urban rapid transit ridership at the station level.Although spatial heterogeneity has been widely acknowledged in spatial data analysis, it has been rarely considered in travel behavior studies.Four models(three global models-ordinary least squares(OLS), spatial lag model(SLM), spatial error model(SEM) and one local model-geographically weighted regression(GWR) model) are estimated separately to explore the relationship between various independent variables and station ridership, and identify the influence of spatial heterogeneity.Using the data of built environment and station characteristics, the results of diagnostic identify evidence the existence of spatial heterogeneity in station ridership for the metro network in Nanjing, China.Results of comparing the various goodness-of-fit indicators show that, the GWR model yields the best fit of the data, performance followed by the SEM, SLM and OLS model.The results also demonstrate that population, number of lines, number of feeder buses, number of exits, road density and proportion residential area have a significant impact on station ridership.Moreover, the study pays special attention to the spatial variation in the coefficients of the independent variables and their statistical significance.It underlines the importance of taking spatial heterogeneity into account in the station ridership analysis and the decision-making in urban planning.

Modeling walking behavior of pedestrian groups with floor field cellular automaton approach

Walking in groups is very common in a realistic walking environment. An extended floor field cellular automaton （CA） model is therefore proposed to describe the walking behavior of pedestrian groups, This model represents the motion of pedestrian groups in a realistic way. The simulation results reveal that the walking behavior of groups has an important but negative influence on pedestrian flow dynamics, especially when the density is at a high level. The presence of pedestrian groups retards the emergence of lane formation and increases the instability of operation of pedestrian flow. Moreover, the average velocity and volume of pedestrian flow are significantly reduced due to the group motion. Meanwhile, the parameter-sensitive analysis suggests that pedestrian groups should make a compromise between efficient movement and staying coherent with a certain spatial structure when walking in a dense crowd.

Evaluation of mobility impact on urban work zones using statistical models

This work correlated the detailed work zone location and time data from the Wis LCS system with the five-min inductive loop detector data. One-sample percentile value test and two-sample Kolmogorov-Smirnov(K-S) test were applied to compare the speed and flow characteristics between work zone and non-work zone conditions. Furthermore, we analyzed the mobility characteristics of freeway work zones within the urban area of Milwaukee, WI, USA. More than 50% of investigated work zones have experienced speed reduction and 15%-30% is necessary reduced volumes. Speed reduction was more significant within and at the downstream of work zones than at the upstream.

Travel modal choice analysis for traffic corridors based on decision-theoretic approaches

The rapid development of multimodal transportation system prompts travellers to choose multiple transportation modes, such as private vehicles or taxi, transit(subways or buses), or park-and-ride combinations for urban trips. Traffic corridor is a major scenario that supports travellers to commute from suburban residential areas to central working areas. Studying their modal choice behaviour is receiving more and more interests. On one hand, it will guide the travellers to rationally choose their most economic and beneficial mode for urban trips. On the other hand, it will help traffic operators to make more appropriate policies to enhance the share of public transit in order to alleviate the traffic congestion and produce more economic and social benefits. To analyze the travel modal choice, a generalized cost model for three typical modes is first established to evaluate each different travel alternative. Then, random utility theory(RUT) and decision field theory(DFT) are introduced to describe the decision-making process how travellers make their mode choices. Further, some important factors that may influence the modal choice behaviour are discussed as well. To test the feasibility of the proposed model, a field test in Beijing was conducted to collect the real-time data and estimate the model parameters. The improvements in the test results and analysis show new advances in the development of travel mode choice on multimodal transportation networks.

Multidimensional Visualization of Bikeshare Travel Patterns Using a Visual Data Mining Technique: Data Cubes

In order to explore the travel characteristics and space-time distribution of different groups of bikeshare users,an online analytical processing(OLAP)tool called data cube was used for treating and displaying multi-dimensional data.We extended and modified the traditionally threedimensional data cube into four dimensions,which are space,date,time,and user,each with a user-specified hierarchy,and took transaction numbers and travel time as two quantitative measures.The results suggest that there are two obvious transaction peaks during the morning and afternoon rush hours on weekdays,while the volume at weekends has an approximate even distribution.Bad weather condition significantly restricts the bikeshare usage.Besides,seamless smartcard users generally take a longer trip than exclusive smartcard users;and non-native users ride faster than native users.These findings not only support the applicability and efficiency of data cube in the field of visualizing massive smartcard data,but also raise equity concerns among bikeshare users with different demographic backgrounds.

Influence of bottleneck on single-file pedestrian flow:Findings from two experiments

In order to investigate the influence of bottleneck on single-file pedestrian flow,we conduct two different bottleneck experiments.The first one is on ring road,while the second one is on straight route.For the first one,the global density is always set to be 1.5 ped/m.The corresponding critical flow rate for the bottleneck activation is about 0.57 ped/s.The data of the detectors set at different locations,including the velocities and time-headways,show that the amplitude of the oscillation of the stop-and-go waves gradually increases during the upstream propagation.Besides,when the measured flow rates are the same,the different situations in the single-file experiments with and without bottleneck are compared and discussed.For the second one,lower flow rates are used and the bottleneck is always activated.In all the runs,the system can reach one stable state,and the time needed is nearly the same.Inside the stable area,the statistics of pedestrians'velocities keeps nearly constant in both time and space.Outside this area,when the waiting time is not long(X=10 s),the phenomenon observed is similar to that found on ring road,e.g.,the statistics of pedestrians'velocities also gradually increases during the upstream propagation.This phenomenon is similar to that found in vehicular traffic flow,which shows the universality of different traffic flows.But when the waiting time becomes longer(X=20 s),this situation will be broken since the actions of many pedestrians become much slower.All these results can facilitate understanding more about the influence of bottleneck on single-file pedestrian flow.

Network traffic flow evolution with battery electric vehicles and conventional gasoline vehicles

In order to investigate the effect of the use of battery electric vehicles on traffic dynamics,the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them.Then,assuming that travelers only focus on their past travel experience,a day-to-day traffic assignment model is established based on reinforcement learning and bounded rationality.In the proposed model,the Bush-Mosteller model,a reinforcement learning model,is modified to calculate path choice probability according to bounded rationality.The modified model updates the path choice probability only if the gap between expected travel time and perceived travel time is beyond the cognitive threshold.Numerical experiments validate the effectiveness of the model and show that traffic flows can converge to the equilibrium in any case of cognitive thresholds and penetration rates of battery electric vehicles.The cognitive threshold has a positive influence on the variation of traffic flows while it has a negative influence on the differences between traffic flows.The adaptation of battery electric vehicles leads to the poor performance of the traffic system.

Evaluating impact of remote-access cyber-attack on lane changes for connected automated vehicles

Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,it can cause significant damage to CAVs or passengers.The primary objective of this study is to model cyberattacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment.Based on the analysis on environmental perception system and possible cyber-attacks on sensors,a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed.The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lanechanging process is then quantitatively analyzed.The evaluation indexes include spatio-temporal evolution of average speed,spatial distribution of selected lane-changing gaps,lane-changing rate distribution,lane-changing preparation search time,efficiency and safety.Finally,the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack.Also,when the traffic system is under cyber-attack,more unsafe back gaps are chosen for lane-changing,especially in the center lane.Therefore,more lane-changing maneuvers are concentrated on approaching the off-ramp,causing severe congestions and potential rear-end collisions.In addition,as the number of cyber-attacked vehicles and the severity of cyber-attacks increase,the road capacity and safety level will rapidly decrease.The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.

Intelligent back-looking distance driver model and stability analysis for connected and automated vehicles

The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.

Steps toward Parallel Intelligence

The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the ''new normal'' of cyber-social-physical systems (CPSS), in which the system behaviors are guided by Merton's Laws. Thus, the ACP-based parallel intelligence consisting of Artificial societies, Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS. © 2014 Chinese Association of Automation.

Solution to multiple attribute group decision making problems with two decision makers

A kind of multiple attribute group decision making （MAGDM） problem is discussed from the perspective of statistic decision-making. Firstly, on the basis of the stability theory, a new idea is proposed to solve this kind of problem. Secondly, a con- crete method corresponding to this kind of problem is proposed. The main tool of our research is the technique o~ the jackknife method. The main advantage of the new method is that it can identify and determine the reliability degree of the existed decision making information. Finally, a traffic engineering example is given to show the effectiveness of the new method.

Measuring accessibility of bus system based on multi-source traffic data

Accessibility is a representative indicator for evaluating the supply of bus system.Traditional studies have evaluated the accessibility from different aspects.Considering the interaction among land use,bus timetable arrangement and individual factors,a more holistic accessibility measurement is proposed to combine static and dynamic characteristics from multisource traffic data.The rationale of the proposed model is verified by a case study of bus system in Shenzhen,China,which is carried out to find the spatial and temporal discrepancy of service of bus system.It is found that the adjustment of bus schedule to time-varying travel demand can affect accessibility of bus system and that Land-use development,average bus speed and bus facilities all have positive effects on accessibility of bus system.These findings provide sig-nificant reference for transport planning and policy-making.The proposed model is not limited to accessibility measuring of bus system,but also applicable to other travel modes.

Estimating model for urban carrying capacity on bike-sharing

As the demand for bike-sharing has been increasing,the oversupply problem of bike-sharing has occurred,which leads to the waste of resources and disturbance of the urban environment.In order to regulate the supply volume of bike-sharing reasonably,an estimating model was proposed to quantify the urban carrying capacity(UCC)for bike-sharing through the demand data.In this way,the maximum supply volume of bike-sharing that a city can accommodate can be obtained.The UCC on bike-sharing is reflected in the road network carrying capacity(RNCC)and parking facilities’carrying capacity(PFCC).The space-time consumption method and density-based spatial clustering of application with noise(DBSCAN)algorithm were used to explore the RNCC and PFCC for bike-sharing.Combined with the users’demand,the urban load ratio on bike-sharing can be evaluated to judge whether the UCC can meet users’demand,so that the supply volume of bike-sharing and distribution of the related facilities can be adjusted accordingly.The application of the model was carried out by estimating the UCC and load ratio of each traffic analysis zone in Nanjing,China.Compared with the field survey data,the effect of the proposed algorithm was verified.

Two Network Structure Indicators for Conventional Public Transit

The significance of network structure indicators for the planning and management of conventional public transit is widely acknowledged.In order to improve and enrich the conventional public transit assessment system,two network structure indicators are proposed.Firstly,according to the obvious defects lying in the traditional no-linear coefficient,the realistic no-linear coefficient γRNL,a modified no-linear coefficient indicator,is put forward,which takes into account the effects of barriers in a city.Secondly,to cover the gap of an indicator which can reflect the coverage homogeneity of a transit network,the length dimension LDis proposed on the basis of Fractal Theory.Finally,a case study is applied to verify the validity and practicability of the two indicators in problem diagnosis using regression analysis.The results validate that γRNLcan evaluate the detour of bus lines more reasonably than the previous no-linear coefficient because it reflects the layout of bus lines,and LDcan represent the rate of change of the network density,adding a new member to the scheme of network structure indicators for public transit.

Optimal Nonlinear Distance Toll for Cordon-Based Congestion Pricing Considering Equity Issue

In order to address the optimal distance toll design problem for cordon-based congestion pricing incorporating the issue of equity,this paper presents a toll user equilibrium( TUE) model based on a transformed network with elastic demand,to evaluate any given toll charge function. A bi-level programming model is developed for determining the optimal toll levels,with the TUE being represented at the lower level.The upper level optimizes the total equity level over the transport network,represented by the Gini coefficient,where a constraint is imposed to the total travel impedance of each OD pair after the levy. A genetic algorithm( GA) is implemented to solve the bi-level model,which is verified by a numerical example.

Investigating the characteristic delay time in the leader–follower behavior in children single-file movement

The single-file movement experiment offered a convenient way to investigate the one-dimensional leader–follower behavior of pedestrians. This study investigated the time delays of children pedestrians in the leader–follower behavior by introducing a time-dependent delayed speed correlation. A total of 118 German students from the fifth grade(aged11–12 years old) and the 11th grade(aged 17–18 years old) participated the single-file experiment. The characteristic delay time for each pedestrian was identified by optimising the time-dependent delayed speed correlation. The influences of the curvature of the experimental scenario, density, age, and gender on the delay time were statistically examined. The results suggested that to a large extent, the revealed characteristic delay time was a density-dependent variable, and none of the curvatures, the age and gender of the individual, and the age and gender of the leader had a significant influence on it. The findings from this study are variable resources to understand the leader–follower behavior among children pedestrians and to build related simulation models.

Bi-Level Programming for the Optimal Nonlinear Distance-Based Transit Fare Structure Incorporating Principal-Agent Game

The urban transit fare structure and level can largely affect passengers’travel behavior and route choices.The commonly used transit fare policies in the present transit network would lead to the unbalanced transit assignment and improper transit resources distribution.In order to distribute transit passenger flow evenly and efficiently,this paper introduces a new distance-based fare pattern with Euclidean distance.A bi-level programming model is developed for determining the optimal distance-based fare pattern,with the path-based stochastic transit assignment(STA)problem with elastic demand being proposed at the lower level.The upper-level intends to address a principal-agent game between transport authorities and transit enterprises pursing maximization of social welfare and financial interest,respectively.A genetic algorithm(GA)is implemented to solve the bi-level model,which is verified by a numerical example to illustrate that the proposed nonlinear distance-based fare pattern presents a better financial performance and distribution effect than other fare structures.