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Large-scale congestion can lead to traffic paralysis, which severely hampers the flow of vehicles and disrupts the normal functioning of urban traffic. Traffic optimization strategies can effectively improve the performance of road networks, but often ignore the impact of regional traffic conditions and equity. This paper presents a novel traffic strategy to solve regional traffic congestion in large cities, particularly focusing on mixed traffic scenarios of connected and non-connected vehicles. The proposed method involves monitoring the traffic condition of the congestion warning community and adjusting the internal access flow within each region. The problem is formulated as a Stackelberg game, with traffic policymakers and road users as the key players. The upper layer aims to control traffic access by issuing a community warning index, with the objective of minimizing congestion warning conditions within the community. This information is then disseminated to connected vehicles which utilize it to generate personalized route guidance, while non-connected vehicles remain unaffected. The lower-level objective is to allocate vehicles in the transportation network in a user-optimal manner. To solve the bi-level programming model, the paper introduces a variable neighborhood search approach based on graph theory. The Frank-Wolfe algorithm is used to solve the lower-level model, with a penalty function introduced to transform the constrained traffic assignment problem (TAP) into an unconstrained TAP. The proposed method is applied using the data of Beijing urban road network and a sensitivity analysis is conducted to examine the impacts of critical parameters, such as regional partitioning and mixed traffic proportion. The results show that the method exhibits improved optimization performance across different parameter settings, effectively utilizing idle links and contributing to a reduction in the occurrence of traffic warning regions. • The regional control strategy prevents the emergence of warning communities. • A bi-level model is developed to solve the traffic diversion of mixed traffic. • A variable neighborhood search algorithm is designed to solve the model. • The control strategies under different critical variables scenarios are analyzed. [ABSTRACT FROM AUTHOR]

With the development of modern cities, urban rail transit has become an indispensable part of residents' travelling mode, and accurate prediction of urban rail transit passenger flow is particularly important. However, due to the non-linearity and non-stability of passenger flow, the low quality of big data and the lack of data make it more and more difficult to predict the passenger flow of urban rail transit. In this paper, we propose a unique model structure that ingeniously integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN) with fused residual network connections, and a self-attention mechanism. The design of this model is primarily to effectively handle complex and low-quality data, which is often prevalent in passenger flow prediction scenarios. To validate the effectiveness of the proposed model, we used data from the Beijing urban rail transit in 2016 for prediction. In the experiments, we performed comparison study and ablation study. The experimental results show that the model proposed in this paper has significantly improved prediction accuracy in both horizontal and vertical comparisons. This outcome substantiates that the proposed model can not only effectively handle complex and low-quality data but also extract short-term features and long-term dependency features of passenger flow well, thereby achieving more accurate predictions. • Integration of GRU, CNN with fused residual connections, and self-attention mechanism constitutes a novel model. • The model is capable of handling complex and lower-quality data, significantly improving the accuracy of predictions. • The model can simultaneously extract short-term features and long-term dependency features from time series. [ABSTRACT FROM AUTHOR]

The connectivity of the rail transit stations is an effective way to evaluate the economic value of the station. In order to explore the influence of the local structure in the complex system of urban rail transit, this paper constructs complex network models based on the Beijing rail transit real network by using the space P and space L methods. The topological structure and global characteristics of the line and station networks are analyzed, and all motifs from 3 to 8 nodes of both networks are obtained using the motif detection algorithm. A subgraph decomposition algorithm for complex network motifs is designed based on five typical subgraphs. The results show that both networks of Beijing rail transit have different typical numbers and distributions of subgraphs, with Y-shaped subgraphs and line subgraphs being the most common for high-node and low-node motifs, respectively. This research proposes a way to assess the connectivity of the rail transit system and has significant reference value for optimizing network functions in the design and planning of rail transit networks. The findings also contribute to the ongoing discussions on network reliability and resilience, as the subgraphs identified in this study could potentially have implications for the network's performance under different scenarios. [ABSTRACT FROM AUTHOR]

As for the increasing traffic pressure in urban cities, it is of great significance to analyze the complex traffic system and grasp the recurrence characteristics of traffic state to better solve the problem of traffic congestion. This paper combines the multi-scale theory and recurrence analysis, which carries out the qualitative and quantitative multi-scale recurrence analysis of the traffic congestion index (TCI) in a period of time in Beijing, China, and further, analyzes the recurrence state of each day in a week, as well as mines the recurrence law. The empirical results reveal that the low-frequency components of the dynamic characteristics of TCI play a major role in the long-term traffic state prediction. The traffic state between weekdays and weekends tends to change, and the state on weekdays is more regular, whereas on Friday, as the critical day for rest days, it is more complex and random. The conclusion of this paper will play a fundamental role in grasping the essential law of Beijing's traffic system and analyzing the traffic congestion problem and the urban traffic system, which has strong practical significance. • The EEMD based recurrence theory reveals the internal structure of traffic data. • Time window is introduced to explore the local characteristics of the traffic system. • The muti-scale recurrence characteristics of the weekly traffic series are analyzed. • The similarities and differences of the daily traffic congestion in a week are given. [ABSTRACT FROM AUTHOR]

Passenger flow distribution in the metro system is fundamental for many applications such as network planning and design, passenger flow forecasting, individual travel activity modeling and emergency response management. However, in most metro systems the smart card automated fare collection (AFC) equipment in Beijing only record when and where a passenger enters and leaves the metro network. Therefore, how to accurately determine passenger flow distribution in unknown travel routes remains a challenging task for the managers. This paper presents a methodology for reconstructing metro passenger flow distribution from large-scale smart card data. A clustering method was first applied to group the travel time of passengers between origin–destination (OD) station pairs into different clusters. Then an approach was proposed that considered both uncertain walking time and transfer time, to estimate the theoretical travel time of all possible routes between the OD pair. An approach to measure the similarity was further employed to match each travel time cluster to a most-likely travel route, and finally obtained the passengers' flow of every route. Compared with two classical methods, the proposed approach was more accurate and efficient. • A data-driven methodology is proposed to obtain metro passenger flow distributions. • A similarity measure model is built to identify possible routes by clustering result. • The travel times are clustered to obtain the possible travel time groups. • Real data from the Beijing metro smart card system is applied to verify our approach. [ABSTRACT FROM AUTHOR]

The growth curves of fractal dimension of urban form take on squashing effect and can be described by sigmoid functions. The fractal dimension growth of urban form in western countries can be modeled by Boltzmann's equation and logistic function. However, these models cannot be well applied to the fractal dimension growth curve of Beijing city, the national capital of China. In this paper, the experimental method is employed to find parametric models for the growth curves of fractal dimension of Chinese urban form. By statistical analysis, numerical analysis, and comparative analysis, we find that the quadratic Boltzmann equation and quadratic logistic function can be used to characterize how the fractal dimension of the urban land-use pattern of Beijing increases in the course of time. The models are also suitable for many cities in the north of China. In order to convert the empirical models into theoretical models, we attempt to construct a model of spatial replacement dynamics of urban evolution, from which the logistic model of urban fractal dimension growth can be derived. The models can be utilized to predict the rate and upper limitation of Chinese urban growth. In particular, the models can be employed to reveal the similarities and differences between the fractal growth of Chinese cities and that of the cities in western countries. • The squashing effect of limit values of fractal dimension leads to sigmoid curves. • Urban fractal dimension growth curves can be modeled by quadratic Boltzmann equation. • Quadratic Boltzmann equation can be reduced to a quadratic logistic function. • Fractal dimension growth curves of urban form fall into two types. • Fractal dimension growth curves indicate two types of spatial dynamics of cities. [ABSTRACT FROM AUTHOR]

As a common human activity in the subway platform, waiting occupies a large amount of passengers' time and plays an important role in the aggregation, circulation, and segregation of passenger flows. The initial spatial distribution of waiting passengers at the subway platform may contribute significantly to the operational efficiency of the subway station. In this paper, we explore the behaviors of waiting passengers by field observations and video recordings at two of Beijing's subway stations under normal conditions and intervention of guide. The data, including distribution of waiting pedestrians on waiting zones, percentages of waiting modes under different density levels, as well as distributions of distance headway, movement speed, and mean waiting time are collected and analyzed in two cases. Quantitative analyses of the collected data of waiting passengers under normal and intervention conditions are conducted to assess the impact of the guide on the behavior of waiting passengers by comparing the indicators of the number of passengers in each waiting zone, percentages of waiting modes, distance headway, as well as mean waiting time. Through analyses of the collected data, the behavioral characteristics of waiting passengers under the tactical and operational levels are revealed. The findings of this paper are beneficial to the organization of waiting passengers and the improvement of operational efficiency of subway stations. • Field observations of waiting passengers are conducted at two subway stations. • The data related to the behaviors and modes of waiting passengers are collected. • The behaviors and characteristics are unveiled by analyzing the collected data. • Quantitative analyses under normal and intervention conditions are conducted. • Joining guides is conducive to improve the waiting conditions. [ABSTRACT FROM AUTHOR]

In recent years, complex network theory has become an important approach to the study of the structure and dynamics of traffic networks. However, because traffic data is difficult to collect, previous studies have usually focused on the physical topology of subway systems, whereas few studies have considered the characteristics of traffic flows through the network. Therefore, in this paper, we present a multi-layer model to analyze traffic flow patterns in subway networks, based on trip data and an operation timetable obtained from the Beijing Subway System. We characterize the patterns in terms of the spatiotemporal flow size distributions of both the train flow network and the passenger flow network. In addition, we describe the essential interactions between these two networks based on statistical analyses. The results of this study suggest that layered models of transportation systems can elucidate fundamental differences between the coexisting traffic flows and can also clarify the mechanism that causes these differences. [ABSTRACT FROM AUTHOR]

The subway and bus together contribute to the structure of public transport network in a metropolis. However, integrated service of public transport is not satisfied due to the lower timetable synchronization between these two modes at transfer stations. In this paper, a double-layer public transport network composed of subway/bus lines and stations is first created. Then, two stages are included in the timetable optimization process. In the first stage, a user equilibrium assignment model and a capacity restriction iterative algorithm are employed to obtain the passenger distribution in the subway–bus public transport transfer network. Based on this distribution, with the consideration of the synchronization and section service level, a mixed-integer linear programming (MILP) model is applied to optimize the timetables of the subway and bus networks in the second stage. Computational results using real Automatic Fare Collection System (AFC) data from the public transport system in Beijing are reported, and these results show that the suggested model is efficient in improving passenger service levels. • Two stages (passenger assignment and timetabling) are in the optimization. • A double-layer network is constructed to describe subway/bus lines and stations. • A user equilibrium is for passengers distribution in the double-layer network. • Four types of space-time relationship of synchronization are considered. • Synchronization and section service level are included in the model objectives. [ABSTRACT FROM AUTHOR]

Free-floating bike-sharing systems allow users to unlock nearby bikes and leave the bikes at any authorized parking area, acting as a time-saving approach for solving the first/last mile problem by connecting trip origins/destinations to metro stations (i.e., bike-and-ride). Inspired by a recent study uncovering the scaling-free phenomenon on the bike-sharing mobility network, this paper addresses the following question: does the power-law distribution also appear in bike-and-ride activities? How does the unbalanced usage pattern of bike-and-ride vary by time and space? To answer the questions, we examine one-week trip orders of free-floating bike sharing in Beijing. We find that the unbalance activities of bike-and-ride show a similar segmented scaling-free feature displaying a first sub-linear regime followed by another super-linear regime on weekdays and weekends. The unbalanced features on weekdays are more substantial than those on weekends and uncovered by the time-varying unbalance in different regimes. Moreover, the unbalanced state corresponding to each metro station obeying non-stationary and dramatically changing peak hours on weekdays suggests that commuting between the suburban and urban areas may be the primary source of the unbalance usage of bike-and-ride in Beijing. Our findings can improve the efficient redistribution of bike-sharing supply and promote the coupling efficiency among transportation modes. • The statistic of access and egress trips connecting with metro stations are explored. • The essential unbalance characteristics of bike-and-ride's temporal and spatial analyses are presented. • Our results improve the efficient redistribution of bike-sharing supply and promote the coupling efficiency among transportation modes. [ABSTRACT FROM AUTHOR]

In recent years, customized bus (CB) service has become an innovative model for public transportation in China. Its ability to provide advanced, timely, personalized, and flexible service has made it very popular. However, previous studies have ignored its diversion effect: How can CB service help conventional buses reduce on-board crowding during rush hours? In this paper, we optimize conventional and CB buses as a whole based on an empirical study in Beijing. We propose a new model to compare the cost of single conventional buses (Single Con) and the integration of conventional and customized buses (Con+ CB). Passenger diversions from conventional buses to CB ones are specifically considered. To do this we explicitly measure the on-board crowding cost of conventional buses by load factor and distance factor. Real-world data from Beijing, China, are employed to assess the model. We obtained the departure arrangements and the corresponding costs under different demand cases. The results are analyzed and compared. Sensitivity analyses are then conducted to study the effects of line-haul distance and load factor on cost. The results show that (1) adding new modes of transportation does not always significantly reduce the total cost of bus service; (2) CB buses can effectively ease congestion on conventional buses only at high demand densities but not at low densities; (3) crowding cost increases caused by the distance factor far exceed those caused by the load factor. • Explicitly measure crowding cost of conventional buses. • Propose cost models of Single Con buses and integration of Con+ CB buses. • Employ the real-world data in Beijing to assess the model. [ABSTRACT FROM AUTHOR]

In this paper, we propose a traffic flow model to study the effects of the real-time traffic state on traffic flow. The numerical results show that the proposed model can describe oscillation in traffic and stop-and-go traffic, where the speed–density relationship is qualitatively accordant with the empirical data of the Weizikeng segment of the Badaling freeway in Beijing, which means that the proposed model can qualitatively reproduce some complex traffic phenomena associated with real-time traffic state. [ABSTRACT FROM AUTHOR]

Knowing the dynamic road risk for pedestrian evacuation and having an efficient evacuation plan play a very important role in the serious disasters such as earthquake, tsunami and hurricane. In this paper, the dynamic road risk for pedestrian evacuation in a densely populated area of Beijing was studied with consideration of different influencing factors. Firstly, the eight influencing factors including road width, node degree, safety betweenness, road resistor coefficient, building threat, pedestrian counterflow, illegal vehicle parking and traffic flow were considered to assess the road risk for pedestrian evacuation. Secondly, based on complex network theory, electric circuit theory and real situation of the roads, the comprehensive assessment function for road risk was developed quantitatively based on the eight influencing factors. Thirdly, we analyzed road risk for pedestrian evacuation considering different situations: current condition, regular condition, and optimal condition; the risk distribution maps were drawn to directly show the risk level. Through assessments, the roads with high risk for pedestrian evacuation were found, and an optimized evacuation plan was obtained and analyzed. This mathematical model can guide the emergency evacuation in real time. The process and the results are essential for improving the efficiency of evacuations which should considerably reduce the possibility of injuries, deaths and other losses in the disaster. [ABSTRACT FROM AUTHOR]

In recent years, extensive efforts have been paid on the investigation of OpenStreetMap (OSM) in developed countries, but little attention has been given to the cities in developing countries. This paper presents the results regarding the evolution of OSM road networks in Beijing, China, from four aspects. First, findings from general analysis indicate that (1) the overall growth pattern could be mainly explained by the increasing number of volunteers and their mapping contributions. Second, findings from geometric analysis suggest: (2) mapping intensity exhibits heavy-tailed pattern both for a certain time period and across time; (3) mapping direction moves from outskirts to downtown distinguished from other regions; and (4) mapping behaviors are mainly constrained by the underlying structure of road networks. Third, results of topological analysis indicate that (5) OSM road networks resemble the growth of real road networks and are undergoing an evolution process depicted by exploration and densification. Last, from the perspective of centrality analysis, (6) two kinds of nodes are identified with the ones accounting for the exploration and the other ones for the densification, which further hints the evolution process of continuous strong exploration accompanied by weak densification. [ABSTRACT FROM AUTHOR]

Abstract: Daily mean temperature records over China during the past 50 years are studied by means of detrended cross-correlation analysis (DCCA). Taking Beijing as a center, we calculate the DCCA cross-correlation coefficient between the temperatures in Beijing and those in other stations. After a statistical significance test, spatial cross-correlation patterns on different time scales are shown in this paper. We find the spatial cross-correlation patterns can vary with time scales. On small time scale of one week to one month, only the temperatures in nearby regions have close relations with that in Beijing, while on larger time scale of intra or inter-seasonal, temperatures in most of the regions, especially in the northeast show high level cross-correlations with that in Beijing. The southwest plateau (including the Tibetan Plateau and the YunGui Plateau) is a special region, where the temperatures take on significant anti-cross-correlations on inter-seasonal scale, but no significant correlations on inter-annual scale. By analyzing these different spatial patterns, we can better understand the influencing climatological processes of different scales. Therefore, DCCA are recommended as a reliable method in detecting the relations between two climatological variables, and further be useful for our understanding of the whole climate system. [Copyright &y& Elsevier]

Bicycle-sharing is an eco-friendly transportation operating model in the context of "Internet Plus" and the sharing economy. It not only meets the short-distance travel needs of residents but has great significance for promoting the sustainable development of urban public transportation. However, a series of problems have appeared in the bicycle-sharing market, such as unreasonable resource allocation, low operating efficiency and management difficulties. Based on booking data and GPS trajectory data in Beijing of Mobike, this paper statistically analyzes the spatial and temporal distribution characteristics of residents' bicycle-sharing trips. Then, we construct a multi-factor influence model of bicycle-sharing demand based on a negative binomial regression and variable selection model, which quantifies a series of factors that influence bicycle-sharing trips, such as population and the regional economy, building land attributes, transportation accessibility, weather, and climatic conditions, etc. The results show that, firstly, there is a spatial imbalance in the distribution of bicycle-sharing demand among different districts in Beijing. Bicycle-sharing demand is mainly concentrated in the six core districts of the city, with more than 80% of all demand. We also find that the bicycle-sharing demand has different distribution characteristics on working days and nonworking days. Compared with nonworking days, residents' demand for bicycle-sharing on weekdays shows obvious peak periods in the morning, noon, and evening. Secondly, factors that have a major impact on the demand for bicycle-sharing include: per capita disposable income, pass facilities, parking lots etc. Among them, factors such as per capita disposable income, pass facilities, parking lots and bus/subway stations have a significant positive influence on bicycle-sharing demand. However, the number of functional zones such as airports, ports and marinas, tourist attraction and automobile sales has a significant negative influence. In addition, a comfortable temperature and good air quality encourage residents to use bicycle-sharing more for travel, while high humidity is not conducive to bicycle-sharing. We suggest that companies and related departments should jointly participate in the regulation and management of the bicycle-sharing industry, in various aspects such as bicycle scheduling, bicycle management and industry systems. In this way, cities can allocate bicycle-sharing resources reasonably and improve overall operating efficiency. The advantages of bicycle-sharing can be better used to promote the sustainable development of urban public transportation in the future. • The temporal distribution of bicycle-sharing demand is imbalanced, including between working and nonworking days, and between peak and off-peak periods of a day. • The spatial distribution of bicycle-sharing demand is imbalanced across the various administrative districts and functional zones. • Factors such as income and pass facilities have positive influence on bicycle-sharing demand, but the functional zones such as airports and ports have negative influence. • The use of bike-sharing is sensitive to the weather and air quality. [ABSTRACT FROM AUTHOR]

Abstract: As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of banking notes, trackings of cell phone users’ locations and trajectories of vehicles. In this paper, we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing. In contrast to most models observed in human mobility data, the taxis’ traveling displacements in urban areas tend to follow an exponential distribution instead of a power-law. Similarly, the elapsed time can also be well approximated by an exponential distribution. Worth mentioning, analysis of the interevent time indicates the bursty nature of human mobility, similar to many other human activities. [Copyright &y& Elsevier]

Emergencies of metro systems have become more frequent in rush hours, which have significant consequences for metro planning, designing, operating, and even the passengers' daily travel. The motivation of this paper is to establish a hybrid metro simulation method with high efficiency and sufficient precision. To this end, a discrete-event simulation method based on a multi-agent model with parallel computing is proposed to estimate the effects of emergencies efficiently. Firstly, the trains' motion algorithms are developed to compute the train speed profile for normal operation and metro emergency operation, respectively. Moreover, three types of agents (passenger, station, and train agents) are classified for rescheduling calculation, and six types of events are defined to discretize the emergency simulation process. Furthermore, a parallel computing method is proposed to accelerate the simulation process. Finally, a case study of the Yizhuang Line in Beijing metro is conducted to verify the effectiveness of the proposed simulation methodology. The results have proved the effectiveness and practicality of the proposed simulation method and the influence of the positions where emergencies occur and the emergency durations upon delays of trains and passengers. • A discrete-event simulation method based on the multi-agent model is developed to investigate the train operation under the metro emergency. • Parallel computing rules corresponding to the discrete-event method are designed to accelerate the simulation. • A case study of the Yizhuang Line in Beijing is conducted where 65 experiments are performed. [ABSTRACT FROM AUTHOR]

Due to the urban industrial transformation and upgrading in China, the relationship between residential and employment spaces in large cities has changed significantly, resulting in an increasingly prominent job–housing separation phenomenon. Based on the taxi GPS trajectory data, this paper firstly analyzes the time and space distribution of job–housing–travel and the commuting characteristics of residents. Then, we construct the evaluation index systems of regional jobs–housing–taxi demand and regional social development level for Beijing. After calculating the weight of each index by using an entropy weight method, we analyze the coupling relationship between regional taxi demand and social development by building the coupling coordination degree model (CCDM). Conclusions are as follows: Firstly, it shows imbalance of space distribution of taxi orders in different administrative and functional areas. The order quantity gradually decreases from the main urban area to the edge, with the most in the living area, accounting for 46.23% of the total orders. Secondly, the daily variation trend of taxi orders in different functional areas is significantly different. During the morning rush hour, taxi orders flow out of residential areas and into office areas; however, during the evening rush hour, they flow out of the office and into the living area. For example, the residence area has a higher proportion of getting on passengers during the morning rush hour, which is 1.46 times that of the getting off ones. Thirdly, a low coupling coordination degree is observed between the regional social development level and the job–housing–taxi demand in Beijing, with a gradually decreasing from the main urban area to the outside, which is mainly caused by the imbalance between supply and demand. Therefore, it is suggested that the relevant government departments should formulate specific control schemes to traffic congestion according to local commuting features, and promote job–housing balance. Meanwhile, the taxi resources are supposed to be allocated reasonably by the striving to information symmetry, to promote the coordination and sustainability between the regional job–housing–taxi demand and social development level. • Administrative and functional areas' taxi orders are spatially imbalanced. • In the morning rush, taxi orders flow from residential to office; the reverse applies during the evening rush. • Congestion has more impact on commuting time than on commuting distance. • A low-degree coupling coordination between RJTD and RSTL in Beijing is observed and decreases outward from main urban areas. [ABSTRACT FROM AUTHOR]

Bike sharing, such as Mobike, are thriving throughout the past three years. Mobike allows users to rent bikes left by previous users. This mobility mode generates self-organized bike distribution over the city. We analyze real data from Mobike bike sharing system in Beijing and Citibike bike sharing system in New York. In this paper, we study the spatial pattern of sharing activity from this new business model. We construct an Origin–Destination spatial network of bike sharing traffic flow based on trip data and study the characteristic of unbalance in the systems. The unbalance of nodes in Mobike bike mobility OD network is revealed to follows a power law distribution with different exponents on weekdays and weekends. We also found that there exists a scaling between maximum unbalance and network degree in both systems. These features of bike sharing can provide a better understanding of sharing mobility behaviors and help to develop more efficient management methods. • We construct Origin–Destination spatial network for bike sharing systems. • We find that the unbalance of Mobike OD Network follows a power law distribution. • We discover a scaling between maximum unbalance and network degree. [ABSTRACT FROM AUTHOR]

Automated Fare Collection (AFC) systems in rail transit services collect enormous amounts of detailed data on on-board transactions. A better understanding of travelers' commuting and transfer behavior based on those massive volumes of AFC data would enable the rail service operators to evaluate their service quality and optimize operation strategies. This paper proposes an efficient and effective data mining procedure to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines in urban rail transit networks. A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. Using up to 28 million AFC smart card transaction data, we match and analyze travelers' trip chains to investigate the commuting trip patterns in terms of spatio-temporal distribution characteristics. An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. Further, the results indicated by stronger association rules (2-frequent itemset and 3-frequent itemset) also provide a better understanding of transfer behaviors, like the frequent transfers among different service lines, and potentially vulnerable stations in the network. The research outcomes can be used to assist rail transit service operators in developing optimal operation strategies like timetabling design to enhance the transfer performance between different rail lines. • An efficient and effective data mining procedure is developed to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines. • An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. • A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. • Experimental results indicated by strong association rules provide a better understanding of transfer coordination in urban rail transit networks. [ABSTRACT FROM AUTHOR]

Travel time is a critical measure for road network traffic conditions, and travel time estimation provides available information for travellers and traffic management. This paper proposes an improved method based on Markov Chains to estimate route travel time by considering spatio-temporal correlation from related links. The method mainly contains three parts. Firstly, in the light of traffic flow data collected from microwave detectors, Gaussian mixture model (GMM) is applied to cluster travel time data under two consecutive links, and thus capture the underlying traffic states. The transition probability matrix is constructed to estimate variations of traffic states over time. Then, link travel time distributions can be estimated from historical observations. Accordingly, we can estimate route travel time distribution by aggregating weighted link travel time distribution based on convolution theory. Finally, a case study including three experiments are used to test the accuracy of travel time estimation, we also compare the estimation performance of proposed model with several traditional methods, and the results indicate that the proposed model is effective and superior to traditional modes based on two indicators: Kullback–Leibler (KL) divergence and Mean Absolute Error (MAE). • An improved method based on Markov Chains is proposed to estimate route travel time. • Gaussian mixture model is applied to cluster travel time data for two consecutive links. • Transition probability matrix is constructed to estimate variations of traffic states. • Route travel time distribution is aggregated from link travel time using convolution theory. • A case study in Beijing City is used to validate the accuracy of travel time estimation. [ABSTRACT FROM AUTHOR]

Based on graph theory, this paper presents a topological heterogeneity and vulnerability analysis of metro networks where transfer constraint is taken into account. The statistical measurements based on minimal transfer times are defined, such as betweenness, the shortest path and the average transfer times. Based on above, on the one hand, heterogeneity of network is analyzed by adopting entropy theory; on the other hand, travel efficiency and connectivity based on effective paths are proposed to measure the topological vulnerability of metro network. The feasibility and rationality of the methods described above are verified through the Beijing metro network. The results show that the topological analysis neglecting transfer constraint would underestimate the heterogeneity and vulnerability of the metro network: the topological heterogeneity and vulnerability of Beijing metro network with considering transfer constraint are smaller than without considering transfer constraint; in the case of considering transfer constraint, with the scale expansion of network, travel efficiency and convenience increase, and topological heterogeneity and vulnerability of network decrease, but the connectivity of metro network is more vulnerable to node-based attack. • Metro network is abstracted as a model transfers constraint is taken into account. • The statistical measurements based on minimal transfer times are defined, such as betweenness and the shortest path. • Travel efficiency and connectivity based on effective paths are proposed to measure the topological vulnerability of metro network. • The feasibility and rationality of the methods are verified through the Beijing metro network. [ABSTRACT FROM AUTHOR]