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Traffic congestion occurs when there is a mismatch between the demand for road use and the available capacity. The volume-delay function (VDF) can quantify the relationship between travel time and the volume of traffic on a particular link, and also provide insight into the state of a traffic system, such as whether it is congested or uncongested. In this paper, we present a VDF model that is based on the fundamental diagram and has two main components: (1) an improved VDF with fewer parameters that can handle both congested and uncongested traffic conditions, based on a fundamental diagram, and (2) a model-based VDF practical calibration framework for practical traffic applications that can determine key parameters for a link in a corridor. Our experiments using corridors in Los Angeles and Beijing demonstrate that our proposed analytical methods effectively calculate road impedance under congested conditions. The results indicate that the proposed model is superior to other existing models in terms of the root mean squared error (RMSE) and mean absolute error (MAE). In addition, our calibrated results indicate that the travel time index (TTI) in Los Angeles is 2.12, in Beijing is 1.74. The model proposed in this paper provides a useful calibration tool for enhancing model performance and improving the accuracy of travel time and speed estimates in traffic assignment. [ABSTRACT FROM AUTHOR]

Rapid suburbanization in China is dramatically reshaping the daily life of urban residents. In China, long-distance commuting, traffic congestion, spatial mismatch and the low quality of life resulting from residential suburbanization are causing widespread concern. This paper compares the changes in time allocation from a time-use perspective, based on two activity-travel survey datasets collected in suburban Beijing in respectively 2007 and 2017. Travelling time to work has increased substantially between 2007 and 2017 and particularly impacted non-working activities. Working and commuting showed to have the greatest impact on time allocated to non-work activities, both in home and out of home, both maintenance and discretionary activities. Furthermore, residents' allocation of time to non-work activities shows to be influenced too by their socioeconomic attributes, such as gender, marital status and household configuration (e.g. extended family). We found that the space–time constraints imposed by household responsibilities leads to greater gender differences in time allocation. This paper provides insight into how urban residents reallocate their time as a result of increasing suburbanization. [ABSTRACT FROM AUTHOR]

Concerns over traffic congestion and environmental pollution have prompted more studies into the impacts of built environment on commuters' mode choice behavior. This paper aims to make a comprehensive analysis of commuters' mode choice behavior with the 5th Household Travel Survey in Beijing. A mixed nested logit model was employed to jointly explore the impacts of mode attributes and built environment variables on commuters' mode choice. The results show that the proposed mixed nested logit model outperforms the contrastive models, i.e., standard multinomial logit model and nested logit model. Estimated results indicate that non-motorized mode commuters pay more attention to OD distance. Land-use mix and density have significant influence on reducing car preference, and only show significant effect in working zone. Furthermore, public modes including bus and subway are regarded as one group that the convenient of either one of them changes will influence commuters' preference on the whole public transportation system. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a significant problem that hinders the proper functioning of the transportation system. Traffic congestion forewarning methods can provide traffic management with accurate congestion prediction information, thus taking timely measures to avoid or alleviate traffic congestion. In this paper, we propose a data-plus-model framework-based expressway congestion forewarning method that can be used in the absence of high-quantity and high-quality data. First, we applied historical traffic flow data to determine the approximate extent of the traffic congestion forewarning and fitted a proper traffic fundamental diagram to characterize the traffic flow. Then, the critical congestion forewarning parameters were obtained according to the slope change of the fundamental diagram. Finally, the proposed method was applied to an expressway section in Beijing to forewarn of traffic congestion with multiday historical traffic data. The experimental results show that the proposed method can effectively analyze the trend of traffic flow accurately and quickly give early forewarning of congestion, which is helpful in reducing the occurrence and persistence of congestion. [ABSTRACT FROM AUTHOR]

How to efficiently identify traffic bottlenecks in the expressway network and implement targeted measures is an important issue to mitigate traffic congestion. In recent years, with the proliferation of smartphones, smartphone-based navigation applications are receiving the worldwide popularity, which are being treated as an extremely rich source of traffic data. In this paper, we are devoted to proposing a novel approach to investigate traffic state and identify recurrent bottlenecks quickly and accurately from macroscopic network perspective, through fusing the data sources of fixed detectors and mobile navigation apps. First of all, this paper plots flow-speed fundamental diagram to derive critical speed, which is treated as the criterion to determine traffic state. Once the criterion has been established, typical bottlenecks in the entire expressway network can be efficiently identified through only utilizing smartphone-based probe speed data. Secondly, three pioneering indicators are put forward to quantify oversaturated traffic state and classify bottleneck patterns on urban expressway network. Applying this methodology, we take Beijing expressway network as a case to identify different patterns of bottlenecks, which are validated to comply with the reality. Moreover, the relationship between critical speed and the associated road segment features is explored and shows the possibility of predicting the critical speed even without flow data. As an end, some recommendations are proposed for improving different types of traffic bottlenecks. [ABSTRACT FROM AUTHOR]

With the traffic congestion problem deteriorating, people increasingly choose urban rail transit (URT) to travel. Although URT alleviates traffic congestion, the long-term operation of a large number of trains leads to huge energy consumption. In order to adapt the major social development concept of "Low carbon", a multi-train energy-saving control collaborative optimization method is proposed in this paper. First, the composition of single train operating conditions is determined by the conversion of operating conditions between stations and the force changes under the premise of ensuring safe and on-time train operation. A single-train energy consumption calculation combinatorial optimization model with the dual control objectives of reducing passengers' average waiting time as well as train traction energy consumption is established. The energy saving control strategy of a single train is investigated by ARMA-Radial Basis Function Neural Network (ARMA-RBFNN) and Genetic Algorithm (GA). Next, the queuing theory is introduced to analyze the variation in passenger waiting time for multiple trains at different arrival intervals. A Deep Reinforcement Learning (DRL) algorithm is designed to obtain the correlation among passenger waiting time, arrival interval and train stopping time. The optimization objective is to minimize the multi-train traction energy consumption and the average passenger waiting time while considering conditions such as train operating safety interval, speed limit, multiple operating state and single train energy-saving models, etc. Then, a multi-train cooperative energy-saving control model is proposed based on the Dragonfly Algorithm (DA). Finally, a case study of Beijing Metro Line 4 is conducted to illustrate the effectiveness of the proposed method. The results demonstrate that the total traction energy consumption and passenger waiting time are reduced by 3.1% and 5 s, respectively, compared with the method of independently optimizing the single-train control strategy. The findings can aid in the development of energy-saving strategies and also provide a basis for energy-saving operation control of multiple trains. [ABSTRACT FROM AUTHOR]

Increasing traffic congestion is a major obstacle to the development of cities. The prediction of traffic flow is very important to city planning and dredging. A good model of flow is able to accurately predict future flow by learning historical flow data. Traffic flow is usually affected by macro and micro factors. At the macro level, the whole city can be divided into different subregions according to the similarity in the traffic flow patterns. At the micro-level, there is a temporal and spatial correlation between the traffic flow of different road sections at di fferent times. In this paper, we propose a multi-mode traffic flow prediction method with Clustering based Attention Convolution LSTM (CACLSTM) to model spatial-temporal data of traffic flow. The framework includes three modules: a convolution LSTM encoding-decoding layer which is used to predict the traffic flow of the next time slice by encoding the historical traffic information, a clustering based attention layer which is able to extract different temporal features by clustering based attention, and an additional factors layer which can integrate weather, wind speed, holidays and other factors to improve the prediction accuracy. The experimental results on Beijing taxis data show that the CACLSTM method performs more effective than the six well-known compared methods. [ABSTRACT FROM AUTHOR]

With the rapid growth of car ownership, traffic congestion has become one of the most serious social problems. For us, accurate real-time travel time predictions are especially important for easing traffic congestion, enabling traffic control and management, and traffic guidance. In this paper, we propose a method to predict urban road travel time by combining XGBoost and LightGBM machine learning models. In order to obtain a relatively complete data set, we mine the GPS data of Beijing and combine them with the weather feature to consider the obtained 14 features as candidate features. By processing and analyzing the data set, we discussed in detail the correlation between each feature and the travel time and the importance of each feature in the model prediction results. Finally, the 10 important features screened by the LightGBM and XGBoost models were used as key features. We use the full feature set and the key feature set as input to the model to explore the effect of different feature combinations on the prediction accuracy of the model and then compare the prediction results of the proposed fusion model with a single model. The results show that the proposed fusion model has great advantages to urban travel time prediction. [ABSTRACT FROM AUTHOR]

With the rapid increase in vehicle ownership, vehicle emissions of pollutants and traffic congestion in mega city are also increasing seriously. It is important to carry out study on heavy-duty diesel vehicles emissions based on real road tests. In this paper, we obtains the large tourist vehicles' second-by-second running data by Portable Emission Measurement System (PEMS) on real road in Beijing, we use the various pollutants data to directly evaluate the real road tourist vehicles emissions that these vehicles merit the state IV and III standard by using fuzzy comprehensive evaluation method. The paper establishes a novel large tourist vehicles emissions comprehensive evaluation method. Result reveals that the real road emission of HC, CO and NOx of the State IV large tourist vehicle is equal to that of the State II. After several years running and oil refilled, the tourist vehicle emissions deteriorated permanently to lower level state standard. [ABSTRACT FROM AUTHOR]

A non‐recurring incident often negatively affects traffic, which is represented as non‐recurrent congestion. However, travellers can usually perceive congestion without knowing the underlying reasons. Accordingly, this paper proposes a data‐driven framework for non‐recurrent congestion detection and interpretation analysis. First, a statistical algorithm named generalized extreme studentized deviate is introduced to detect non‐recurrent congestion by comparing the current traffic speed with the speed threshold learned from historical data. The case study in Beijing shows that the proposed generalized extreme studentized deviate outperforms other prevailing algorithms in terms of detection rate, false alarm rate, and mean detection time. Second, data mining and natural language processing technologies are implemented on data collected from Sina Weibo, a Chinese microblog site akin to Twitter, to classify non‐recurring incidents that may be associated with non‐recurrent congestion, including traffic accident, road construction, concert, special sport (marathon), and commercial activity. Results show that overall classification accuracy reaches 95%. Finally, the association relationship between the detected non‐recurrent congestions and incidents is established via spatiotemporal information matching. This information matching provides a bidirectional verification. On the one hand, nearly 58% of non‐recurrent congestion can be explained by incident‐related (IR) microblogs. On the other hand, an average of 62% of IR microblogs can be traced by nearby non‐recurrent congestions. This paper suggests that social media can be used as a secondary source and integrated with traffic data to enhance the understanding of non‐recurrent congestion. [ABSTRACT FROM AUTHOR]

Summary: Taxi GPS trajectories can be mined and used to optimize urban traffic scheduling. The optimization of traffic scheduling is important, especially in megacities such as Beijing. In this paper, we analyze the traffic features in Beijing by mining taxi GPS trajectories. We define the Congestion Coefficient of each edge of the taxi trajectory as the consumed time of a taxi running over a unit of distance. By analyzing the distribution of congestion coefficients of all taxi trajectories, we can observe that, on working days, (1) the congestion coefficient is between 0 and 2 (average speed is greater than 0.5 m/s) and is acceptable to taxi drivers, (2) the morning rush hours are 7:00 ∼ 10:00, (3) the evening rush hours are 17:00 ∼ 20:00, and (4) the traffic congestion in the morning rush hours is worse than that in the evening rush hours; on the weekend, (1) the congestion coefficient is less than 0.2 (average speed is greater than 5 m/s) and is acceptable to taxi drivers; (2) compared with the traffic congestion on working days, there are no significant morning rush hours on the weekend; and (3) the period of the time between 13:00 and 15:00 could be considered the traffic rush hours on the weekend. These findings can be used to improve urban traffic management. [ABSTRACT FROM AUTHOR]

Recent advance in VMS technology has made it viable to ease traffic congestion and improve road traffic efficiency. However, the drivers' low compliance with the posted information may limit its performance to ease traffic congestion and improve traffic safety. This paper explores drivers' attitude to the service quality of VMS system resulted from the identified predominant influencing factors. A questionnaire is developed and used for surveying 9,600 drivers in Beijing, China. The collected data are analyzed with a multiple indicators and multiple causes (MIMIC) model considering different driver categories (e.g., private car driver, office car driver, taxi driver). The results show that the causal relationships between latent variables and socio-demographic characteristic is significant. Driving frequency, attitude towards contents of VMS, drivers' decision-making and the effectiveness of VMS message can directly and indirectly affect driver's perceived quality of service. The attitude towards formats of VMS indirectly affect their QoS resulting from the effectiveness of VMS message, while there is no indirect impact for taxi drivers. Besides, the drivers' decision-making directly affects the perceived quality of service for private car drivers and office car drivers, but there is no impact for taxi drivers. The findings of this study can provide guidance and reference for urban authorities to perform the relevant actions required to meet user expectations. [ABSTRACT FROM AUTHOR]

This paper focuses on the distribution of passenger flow in Huoying Station, Line 13 of Beijing subway system. The transformation measures taken by Line 13 since operation are firstly summarized. Then the authors elaborate the facilities and equipment of this station, especially the node layout and passenger flow field. An optimization scheme is proposed to rapidly distribute the passenger flow in Huoying Station by adjusting the operation time of the escalator in the direction of Xizhimen. The authors adopt Queuing theory and Anylogic simulation software to simulate the original and the optimized schemes of Huoying Station to distribute the passenger flow. The results of the simulation indicate that the optimized scheme could effectively alleviate the traffic congestion in the hall of Huoying Station, and the pedestrian density in other places of the hall is lowered; passengers could move freely in the hall and no new congestion points would form. The rationality of the scheme is thus proved. [ABSTRACT FROM AUTHOR]

The HOV carpooling lane offers a feasible approach to alleviate traffic congestion. The connected vehicle environment is able to provide accurate traffic data, which could optimize the design of HOV carpooling schemes. In this paper, significant tidal traffic flow phenomenon with severe traffic congestion was identified on North Beijing road (bidirectional four-lane) and South Huaihai road (bidirectional six-lane) in Huai'an, Jiangsu Province. The historical traffic data of the road segments were collected through the connected vehicle environment facilities. The purpose of this study is to investigate the effect of adopting two HOV schemes (regular HOV scheme and reversible HOV carpooling scheme) on the urban arterial road under connected vehicle environment. VISSIM was used to simulate the proposed two HOV carpooling schemes at the mentioned road segment. The simulation results showed that the reversible HOV carpooling scheme could not only mitigate the traffic congestion caused by traffic tidal phenomenon but also improve the average speed and traffic volume of the urban arterial road segment, while the regular HOV scheme may exert a negative impact on the average speed and traffic volume on the urban arterial road segment. [ABSTRACT FROM AUTHOR]

Short-term traffic prediction (STTP) is one of the most critical capabilities in Intelligent Transportation Systems (ITS), which can be used to support driving decisions, alleviate traffic congestion and improve transportation efficiency. However, STTP of large-scale road networks remains challenging due to the difficulties of effectively modeling the diverse traffic patterns by high-dimensional time series. Therefore, this paper proposes a framework that involves a deep clustering method for STTP in large-scale road networks. The deep clustering method is employed to supervise the representation learning in a visualized way from the large unlabeled dataset. More specifically, to fully exploit the traffic periodicity, the raw series is first divided into a number of sub-series for triplet generation. The convolutional neural networks (CNNs) with triplet loss are utilized to extract the features of shape by transforming the series into visual images. The shape-based representations are then used to cluster road segments into groups. Thereafter, a model sharing strategy is further proposed to build recurrent NNs-based predictions through group-based models (GBMs). GBM is built for a type of traffic patterns, instead of one road segment exclusively or all road segments uniformly. Our framework can not only significantly reduce the number of prediction models, but also improve their generalization by virtue of being trained on more diverse examples. Furthermore, the proposed framework over a selected road network in Beijing is evaluated. Experiment results show that the deep clustering method can effectively cluster the road segments and GBM can achieve comparable prediction accuracy against the IBM with less number of prediction models. [ABSTRACT FROM AUTHOR]

Traffic congestion is now nearly ubiquitous in many urban areas. The improvement of road infrastructure is an effective way to ease traffic congestion, especially the key road links. So, it is a fundamental and important step to identify the key link for improving transportation efficiency. However, most approaches in the current literature use simulated data and need many assumption conditions. The result shows the low comprehensibility and the bad exactitude. This paper provides a new identification method of key links for urban road traffic network (URTN) based on temporal-spatial distribution of traffic congestion. The method involves identifying congestion state, computing time distribution of congestion state and determining key road link. By the cluster analysis of the history field data of URTN, the threshold to determine the traffic congestion of each link can be obtained. Then the time-interval of the traffic congestion can be computed by median filtering. At last, the time-interval coverage is defined and used to determine the target road link whether it is a key road link or not. The method is validated by a real-world case (Beijing road traffic network, BRTN). The result shows the feasibility and accuracy. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the key issues relating to sustainability and livability in many large cities. In particular, the situation in the growing megacities of developing countries has been worsening and is now attracting considerable attention from researchers and politicians. An understanding of the spatio-temporal patterns of this congestion is necessary in order to formulate effective policies to relieve it. Much of the research to date has focused on single districts for relatively short periods (days or weeks) using GPS, while long-term analysis of spatial and temporal patterns of traffic congestion at the city level has been rare. The aim of this paper is to help fill this gap in the literature by applying a big data analytic approach to a sample of 10.16 million records of traffic congestion indexes for 233 roads in the Beijing area over a six-month period. This analysis revealed four typical traffic congestion patterns in Beijing, which can be described as the weekend mode, holiday mode, weekday mode A, and weekday mode B. Each of these patterns possesses unique spatial and temporal characteristics. Compared with working days, on which congestion is regular and agglomerated, weekends and holidays are characterized by long-lasting congestion peaks throughout the day. Non-commuting travel on weekends and holidays, including trips for tourism, shopping, entertainment, and children's after-school activities, are major contributors to traffic congestion of the weekend and holiday mode. Owing to poor jobs-housing balance, the suburban new towns and job centres had relatively higher congestion than other areas. These findings shed significant light on geographical patterns of traffic congestion in growing megacities. • Traffic congestion is a key issue relating to urban sustainability. • This paper apples a big data analytic approach to a sample of 10.16 million records of traffic congestion. • There are four typical traffic congestion patterns in Beijing. • No-commuting travels on weekends and holidays are major contributors to traffic congestion. • The suburban new towns and job centres had relatively higher congestion. [ABSTRACT FROM AUTHOR]

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system. [ABSTRACT FROM AUTHOR]

With the development of social economy and health care technology, people are expected to live longer, and the world's population tends to be ageing. As one of the services for elderly people, the concessionary bus travel programme provides a kind of social welfare for elderly people, while peak-hour traffic pressures (e.g., traffic congestion, traffic injuries, and travel discomfort) faced by elderly people have been ignored. This paper attempts to help society create a better travel environment for elderly people by analysing their travel behaviours. Specifically, using household survey data from Beijing, the statistical analysis is used to paint a more detailed picture of daily travel experience of elderly people with regard to departure times, travel purposes, travel mode choices, etc. Then, a nested logit model is employed to interpret travel patterns of elderly people. The results show that the bus plays the most important role in motorized travel of elderly people. Furthermore, the free bus programme has induced only 4.85% of bus travel among elderly people, indicating that the programme has little effect on elderly people's bus choices. Based on the results of the analysis, several options are proposed in this paper to help society adapt to the changing demographics. [ABSTRACT FROM AUTHOR]

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip planning and rerouting decisions in transportation systems. Over the decades, there have been many studies focusing on traffic congestion estimation and prediction with different statistical approaches (e.g., Markov chain) and machine learning models (e.g., clustering, Bayesian networks, and artificial neural networks). However, there is a lack of a unified framework to address the mechanisms of different models and integrate the advantages of different methods through combinations. This paper introduces the FD-Markov-LSTM model, a hybrid interpretable approach that combines the fundamental diagram (FD), Markov chain, and long short-term memory (LSTM). The aim is to estimate and predict traffic states by integrating statistical data in both congested and uncongested scenarios. The FD-Markov-LSTM model leverages the FD to identify hierarchical traffic states and utilizes the Markov process to capture the probabilistic transitions between these states. We employ the LSTM model to further capture the residual time series produced by the Markov chain model (assuming a memoryless property) to enhance the estimation and prediction performance. The proposed model's accuracy in estimating and predicting traffic flow is evaluated using empirical data from three case studies conducted in Beijing and Los Angeles. The results highlight a significant improvement in accuracy compared to classical benchmark models such as the Markov model, ARIMA model, k-Nearest Neighbor model, Random Forest model, and LSTM. Specifically, the FD-Markov-LSTM model achieves reductions of over 39% in mean absolute error, 35% in root mean squared error, and 7.4% in mean absolute percentage error. These results clearly demonstrate that the FD-Markov-LSTM model outperforms the benchmark models, enabling more precise predictions of traffic flow. [ABSTRACT FROM AUTHOR]

• Owning a car is not related to increased life satisfaction in Beijing. • However, owning more than one car weakly contributes to higher life satisfaction. • A more expensive car does not influence travel and life satisfaction. • Infrequent car use is related to higher travel and life satisfaction. • Attitudes towards cars confound the effect of car ownership and use on well-being among car owners. Promoting subjective well-being is an important goal in urban policy. Transportation scholars increasingly evaluate transportation systems based on people's subjective well-being. This study joins this strand of efforts to answer the following research question: how do constrained car ownership and car use influence travel and life satisfaction? Empirical data were collected in Beijing where various policies had been implemented to curb the rapid growth of car ownership and mitigate traffic congestion. We answer the question by examining the effects of car ownership and car usage variables on travel and life satisfaction. Our study shows that owning multiple cars increases vehicle users' life satisfaction, but owning a car is not related to increased life satisfaction. Purchasing a more expensive car and driving cars more frequently in daily life could not increase people's life satisfaction, but infrequent car use contributes to higher travel and life satisfaction. Among vehicle users, using cars to commute is related to higher travel satisfaction but not higher life satisfaction. In contrast, using cars for non-work trips is related to higher life satisfaction but not travel satisfaction. We also find that attitudes towards the instrumental and affective roles of cars are positively related to travel and life satisfaction among car owners. Results of this paper suggest the importance of promoting sustainable transportation and behavioral education in cities like Beijing. [ABSTRACT FROM AUTHOR]

This study examines the effectiveness of the Odd-Even policy in Jakarta from 2017 to 2022, comparing it with similar measures in Beijing from 2013 to 2017. Both megacities face severe traffic congestion due to rapid urbanization, population growth, and increased vehicle ownership. Jakarta's congestion is exacerbated by population density, inadequate public transportation, and high private vehicle ownership, leading to prolonged travel times, air pollution, and economic losses. Similarly, Beijing struggles with traffic congestion due to its massive population and rapid economic development, resulting in gridlock, air pollution, and decreased quality of life. The research aims to understand the Odd-Even policy's impact on traffic flow, emissions, and public perception in both cities, using qualitative methods and diverse data sources. It also identifies obstacles to policy effectiveness, such as enforcement, infrastructure, and public compliance, offering recommendations for policymakers and urban planners to improve sustainable transportation and environmental management. Overall, the study contributes insights into congestion mitigation in megacities, aiding policymakers in enhancing urban life quality. [ABSTRACT FROM AUTHOR]

How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00–8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance. [ABSTRACT FROM AUTHOR]

The majority of studies on road traffic flow prediction have focused on the flow of passenger cars or the flow of traffic as a whole, which ignore the significant impact of trucks with different sizes and operational characteristics on traffic flow efficiency. Therefore, in this paper, we focus on truck traffic flow and propose a Multifeatures Spatial-Temporal-Based Neural Network model (M-BiCNNGRU) to improve its prediction. The proposed model not only comprises conventional temporal characteristics and spatial relationships but also includes a range of multifeatures. These multifeatures include policy limit, optimal time delay, road resistance, and traffic congestion state. The impacts of upstream and downstream road sections are considered on the spatial relationship by using a Convolutional Neural Network (CNN). A Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to account for the temporal characteristics. To evaluate the proposed model, traffic flow data were collected from a major expressway in Beijing and the results were compared with those derived from existing models. The results show that the prediction accuracy of the BiCNNGRU model, with spatial-temporal characteristics, and the M-BiGRU model, with multifeatures and temporal, is, respectively, 4.13% and 2.15% greater than that of the Bi-GRU model, with temporal characteristics. The prediction accuracy of the proposed M-BiCNNGRU model is 92.86%, which is 7.12% greater than that of the Bi-GRU model and 13.83% greater than that of the Support Vector Regression (SVR) model. In general, therefore, the proposed M-BiCNNGRU model, which combines multifeatures, temporal characteristics, and spatial relationships, can significantly improve accuracy in predicting truck traffic flow. [ABSTRACT FROM AUTHOR]

Recently, traffic problems such as congestion and emission have been increasing. Transit travel is considered to be one of the effective ways to relieve these problems. The attractiveness of transit travel can be improved by integrating operational strategies with multitype services. Therefore, a study focusing on tapping the potential of integrated operational strategies, which combine local services, skip-stop services, and short-turning services, considering emissions under heterogeneous demand, was conducted. Two types of integrated service patterns were proposed. Pattern 1 comprised local services, skip-stop services, and short-turning services, all operating simultaneously and independently, while Pattern 2 comprised the combined local services and skip-stop services with a flexible route length. The estimation of heterogeneous travel demand for different services and high-resolution bus emissions based on short trips between stops and at stops was conducted. The optimized models to minimize system costs considering passengers, agencies, and environmental interests were developed to explore the effectiveness of integrated operational strategies for performing the transit service. The real-world cases of Bus Route 4 and Route 543 in Beijing, China, were studied. The performances of the proposed strategies under the two integrated service patterns were compared with the baseline. The results showed that the total costs of two integrated service patterns are reduced significantly, with a maximum reduction of above 30%. Moreover, under the route of limited vehicles and denes travel demand, Pattern 2 implementing skip-stop services in a flexible route length had more advantages over Pattern 1, where all three services existed simultaneously. The finding can be used to design the preferred transit development strategy and improve the service quality. [ABSTRACT FROM AUTHOR]

After decades of rapid economic growth, the municipality of Beijing is now facing serious urban transport challenges with rapid motorization and travel demand growth. Designing and implementing efficient and equitable urban transport policies are essential to achieve the sustainable urban mobility target of the city. The success of transport management around the 2008 Olympic Games has left policymakers with the confidence to develop various efficient policies for congestion mitigation. Following a resume of the characteristics of fast development, this paper investigates the transport management measures used following the Olympic Games. These are analyzed using an ASIF approach within the framework of urban transport sustainability. The policies examined include a priority to develop mass transit systems, small passenger car purchase policy, a staggered peak-hour plan, new charging policies for parking, and traffic restrictions based on license plate numbers. Proposals for enhancing the effectiveness of these policies and recommendations on future policy development are also presented. [ABSTRACT FROM AUTHOR]

• The effectiveness of existing traffic measures in Beijing is investigated. • A survey on drivers' opinions and attitudes towards the traffic situation is given. • Most respondents think congestion is still serious in Beijing. • The perceived impact of congestion on personal car use is relatively low. • The symbolic meaning of personal cars has large impact on willingness to change. Facing serious challenges of traffic congestion and air pollution, Beijing has implemented a series of traffic polices. In this paper, we first investigate the effectiveness of existing traffic measures in Beijing and analyse the underlying factors from the perspective of drivers, which provide insights for the traffic regulations in other cities worldwide. While the car ownership restriction has effectively limited the total number of vehicle in Beijing, the effect of the car use restriction only lasts for a short period. A survey on drivers' opinions and attitudes towards the traffic situation and a potential measure, i.e., tradable driving credit (TDC), is conducted. Although traffic data shows that congestion improved and traffic was slightly congested in recent years in Beijing, most respondents believe congestion is still serious in Beijing. However, they think the impact of traffic congestion on their personal car use is relatively low, which could partly explain why so many travellers still depend on driving given the relative convenient transit facilities in Beijing. In addition, a large proportion of respondents treat personal cars as a representation of social status and think driving bring them pleasures, which indicates that it may be hard to further decrease the share of car trips in Beijing. According to their attitudes and social-economical characters, respondents' willingness on their changes of travel behaviours under TDC is investigated. The results show that individuals' social-economic characters such as income level, education and family size and the symbolic meaning of personal cars have significant influence on respondents' willingness to switch to other transport modes. [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]

Summary: The travel demand is of vital importance for transport planning. Especially in rush hours, the commuters aggregate in certain areas, resulting in traffic jams, which hinder the normal operation of the urban traffic. The key issue to solve the problem is to find out the demand of the passengers and accordingly arrange the transit resource more reasonably. This paper proposes a framework that provides a shuttle bus solution to satisfy the travel demand of the gathered passengers in rush hours according to their travel history obtained by smartcard data. Firstly, an aggregation algorithm basing on Clustering by fast search and find of density peaks (CFSFDP) is presented to highlight the area, which consists of adjacent bus stations with high passenger flow, addressed as spark region. Secondly, group travel pattern (GTP) is put forward to describe the travel trend on a city‐wide scale, which reveals the common travel demand of the commuters. Lastly, an algorithm named Variable Visibility Path Optimization Algorithm based on ant colony algorithm is proposed to make schedule solution of shuttle bus according to GTP basing on the historical running information of the bus. The experiment basing on the bus passenger flow data collected by AFCS in Aug 2014 from Beijing shows that our method helps to ease the traffic aggregation effectively and practically and offer reference to the bus scheduling issue. [ABSTRACT FROM AUTHOR]

Motivated by traffic congestion and air pollution, Beijing is one of several major cities to restrict vehicle ownership by requiring residents to win a lottery for the right to obtain an additional car. We examine the welfare cost of preventing people from owning cars because of misallocation: under a lottery, some individuals with low willingness to pay (WTP) for cars can obtain cars, while other individuals with high WTP cannot. We estimate welfare costs using a new contingent valuation method survey of Beijing lottery participants which we designed and conducted explicitly for this purpose. We find that restricting vehicle ownership reduced private welfare by 26 billion yuan. Back-of-the-envelope calculations suggest that the benefits of lower congestion and pollution roughly equal the costs. Our WTP estimates indicate a net welfare gain of approximately 32 billion yuan if Beijing's lottery were replaced with an auction, which is similar to previous estimates. [ABSTRACT FROM AUTHOR]

In general, socioeconomic factors play an essential role in traffic congestion. However, there is still doubt whether traffic congestion fluctuates indefinitely with the change in socioeconomic factors. Using a time analysis unit–based data envelopment analysis (DEA), this study aims to demonstrate urban development efficiency from the perspective of traffic congestion. We employ a linear model to illustrate the effect of internal correlation between independent variables on the model's accuracy. Through the Gray correlation model, we explore the correlation between socioeconomic factors and the traffic congestion index and select the key evaluation indices of development efficiency. Because the urban development efficiency evaluation belongs to a multi-input and single-output system, we introduce DEA to build a quantitative model for the coupling situation between socioeconomic factors and traffic congestion and to identify the factors with high redundancy. The results show that Beijing's urban development over the past two decades has been relatively efficient in terms of traffic congestion. This means that the change in relevant socioeconomic factors has played a rather good role in reducing traffic congestion. The discoveries presented here can make us understand the economic principle of socioeconomic factors to reduce traffic congestion and lay the foundation for adjusting socioeconomic factors' utilities in modeling traffic congestion. [ABSTRACT FROM AUTHOR]

To improve traffic efficiency and utilization of road resources and alleviate traffic congestion caused by imbalance of bidirectional traffic flow, in view of the conversion conditions of reversible lane function, the operating characteristics of associated intersections under dynamic reversible lanes are analysed in terms of capacity, and a reversible lane control model is constructed based on short-term traffic flow prediction. On this basis, the reversible lane segment clearing time and upstream and downstream signal control strategies under different states are studied. The collaborative control model of reversible lane clearing time and signal timing of associated intersections is established to obtain the optimal time for reversible lane function switching. Finally, using Chaoyang Road, Beijing, as an example, the effectiveness of the proposed model is verified by the simulation indexes of average vehicle delay and reversible lane clearing time. The results show that the optimized clearing efficiency exceeds 15% and the optimized average vehicle delay is reduced by more than 10%. Combined with the future traffic state, the traffic capacity and saturation flow are greatly improved, and the intelligent reversible lane control is better achieved. [ABSTRACT FROM AUTHOR]

This paper proposed a new method to describe, compare, and classify the traffic congestion points in Beijing, China, by using the online map data and further revealed the relationship between traffic congestion and land use. The data of the point of interest (POI) and the real-time traffic was extracted from an electronic map of the area in the fourth ring road of Beijing. The POIs were quantified based on the architectural area of the land use; the congestion points were identified based on real-time traffic. Then, the cluster analysis using the attributes of congestion time was conducted to identify the main traffic congestion areas. The result of a linear regression analysis between the congestion time and the land use showed that the influence of the high proportion of commercial land use on the traffic congestion was significant. Also, we considered five types of land use through performing a linear regression analysis between the congestion time and the ratio of four types of land use. The results showed that the reasonable ratio of land use types could efficiently reduce congestion time. This study makes contributions to the policy-making of urban land use. [ABSTRACT FROM AUTHOR]

Alleviating traffic congestion is one of the main challenges for the Internet of Vehicles (IoV) in smart cities. Many congestion pricing systems have been proposed recently. However, most of them focus on punishing the vehicles that use certain roads during peak hours, neglecting the proven fact that rewards can encourage drivers to follow the rules. Therefore, in this paper, we propose a new congestion pricing system based on reward and punishment policies for the IoV in a smart city environment, where the vehicles are rewarded for voluntarily choosing to take an alternative path to alleviate traffic congestion. The proposed system is implemented using vehicular ad hoc networks, which eliminate the need for installing a costly electronic toll collection system. We propose a new virtual currency called T-Coin (traffic coin), that is used to reward the vehicles for their positive attitude. T-Coin is also used in the tender between vehicles to manage the road reservation process. The proposed system uses dynamic pricing to adapt to peak-hour traffic congestion. Using simulated traffic on a real map of Beijing city, we prove the usefulness of T-Coin as a traffic congestion pricing system. [ABSTRACT FROM AUTHOR]

With ongoing urbanization, traffic congestion and the air pollution it induces are worsening. Using a system dynamics (SD) approach, this study constructed a driving-restriction policy model to explore the effects of different stages of policy implementation on variables such as traffic congestion, emissions, and parking demand. Medium- and long-term dynamic simulation showed that the effect of the policy was obvious in the initial stage but gradually weakened in the medium term, leading to a "fading" effect on traffic-congestion alleviation; a "rebound" effect was even observed at the end of the simulation. Thus, the policy will not effectively reduce traffic congestion in the long term and will induce a new demand for car purchases, resulting in paradoxical effects, which will aggravate parking demand, congestion, and pollution. Yet, it was also found that introducing penalty policies and an air pollution charging fee could weaken the paradoxical effects and compensate for some defects of the policy. Such strategies could help reduce emissions, traffic congestion, parking demand, the number of illegal trips, and the overall number of vehicle trips. These findings can provide not only a theoretical basis for further research but also practical guidance for policy improvement. [ABSTRACT FROM AUTHOR]

Sequential pattern mining (SPM) is a major class of data mining topics with a wide range of applications. The continuity and uncertain nature of trajectory data make it distinctively different from typical transactional data, which requires additional data transformation to prepare for SPM. However, little research focuses on comparing the performance of SPM algorithms and their applications in the context of trajectory data. This study selected some representative sequential pattern mining algorithms and evaluated them with various parameters to understand the effect of the involved parameters on their performances. We studied the resultant sequential patterns, runtime, and RAM consumption in the context of the taxi trajectory dataset, the T-drive dataset. It was demonstrated in this work that a method to discretize trajectory data and different SPM algorithms were performed on trajectory databases. The results were visualized on actual Beijing road maps, reflecting traffic congestion conditions. Results demonstrated contiguous constraint-based algorithms could provide a concise representation of output sequences and functions at low m i n _ s u p with balanced RAM consumption and execution time. This study can be used as a guide for academics and professionals when determining the most suitable SPM algorithm for applications that involve trajectory data. [ABSTRACT FROM AUTHOR]

There is still doubt about how to accurately distinguish the objective and subjective factors that affect traffic congestion. This study aims to re-examine the role of socioeconomic factors and governance policies in the variation of urban traffic congestion from a global perspective at the macro-social development level. The heterogeneous characteristics of the traffic congestion index under the influence of changes in governance policies and variations of social–economic factors are distinguished through a residual trend model. Different time scenarios are addressed in the study to assess the respective contribution of socioeconomic factors and governance policies to variations of traffic congestion, which is helpful for identifying the most destructive factors and the most effective combination of governance policies. Taking Beijing City as the case, the results show that the social-economic factors and governance policies jointly drive congestion fluctuations, but the contribution of governance policies is greater. In addition, governance policies play an important role in alleviating traffic congestion, but unfortunately, an unreasonable combination of governance policies is also the chief culprit for the rise of the traffic congestion index in certain periods. The findings presented here can make us understand macro-mechanisms in response to urban traffic congestion and provide evidence for formulating regional development policies. [ABSTRACT FROM AUTHOR]

Owing to the current development trend of globalization and informatization, traditional transportation technologies and means can no longer meet the needs of economic and social development. Thus, intelligent transportation will be an inevitable and revolutionary choice for the development of the transportation industry. As a subsystem of the intelligent transportation system, the electronic nonstop toll collection (ETC) system provides a good solution to the problems of traffic congestion, environmental pollution, energy consumption, and low toll operation efficiency caused by expressway toll collection. This high-technology system is utilized in many fields, such as radio communication and computer and automatic control in the transportation field. China's expressway ETC systems have evident effects on congestion reduction at toll stations. Although the advantages of ETC lanes are significant, and China actively implements ETC-handling policies, some drivers are reluctant to install ETC on-board systems or they have negative experiences after handling them. According to a survey, existing literature lacks research on the factors affecting user satisfaction with ETC. On the basis of the questionnaire data of ETC user experience in Shanghai and Beijing, we comprehensively investigate the reasons of banking, ETC equipment, and drivers' personal factors and travel characteristics to establish an ordinal logistics model. Through cluster analysis, drivers in the two cities are divided into three groups, and the significance of related variables is further analyzed. This research can help us understand the degree of influence of different factors on user satisfaction with ETC to a certain extent. Moreover, effective methods and measures can be used to promote the popularity of ETC vehicle equipment and the utilization rate of ETC-dedicated lanes. [ABSTRACT FROM AUTHOR]

Aiming to alleviate traffic jams, many traffic management strategies/policies are adopted to nudge travelers to re-arrange their departure time or switch from driving to public transit or non-motorized mode. Analytical travel behavior model is needed to predict travelers’ departure time choice and mode switch under such strategies. In this paper, we developed an agent-based model for travellers’ choices of mode and departure time. Departing from the traditional utility maximization theory, this model focuses on the decision-making process based on imperfect information, bounded and distinctive rationalities. In the modeling framework, travelers accumulate experiences and update their spatial and temporal knowledge through a Bayesian learning process. Before making a trip, travelers decide whether to search for alternative departure time and/or travel mode according to their expected search gain and cost. When an additional search happens, travelers decide whether or not to switch to the new departure time and travel mode according to a series of decision conditions. Both the search and decision processes are represented by production (if–then) rules derived from a joint revealed/stated-preference survey data collected in Beijing. Then the agent-based model is applied to evaluate congestion charge policies with various demand scenarios in the 2nd ring road of Beijing. Results suggest that the model can display the peak spreading and mode switch process practically. [ABSTRACT FROM AUTHOR]

In the recent past Beijing has experienced rapid development. This growth has been accompanied by many problems including traffic congestion and air pollution. Understanding what stimulates urban growth is important for sustainable development in the coming years. In this paper, we first estimate a binary auto-logistic model of land use change, using physical and socioeconomic characteristics of the location and its access to major centers within the city as predictors. We find that variables determining regional access, like time distance to the city center, the Central Business District (CBD), industrial centers, employment centers, and the transportation system, significantly impact urban land conversion. By using measures of access to predict land use change we believe that we can better understand the planning implications of urban growth not only in Beijing but other rapidly developing cities. [ABSTRACT FROM AUTHOR]

To reduce traffic congestion and protect the environment, license plate control (LPC) policy has been implemented in Beijing since 2011. In 2019, 100,000 vehicle license plates were distributed, including 60,000 for electric vehicles (EVs) and 40,000 for gasoline vehicle (GVs). However, whether the current license plate allocation is optimal from a social welfare maximization perspective remains unclear. This paper proposes a two-level Stackelberg game, which portrays the interaction between vehicle applicants and the government to quantify the optimal number of EV license plates under the LPC policy in Beijing. The equilibrium number of EV license plates derived from the Stackelberg model is 58,800, which could increase the social welfare by 0.38%. Sensitivity analysis is conducted to illustrate the impact of important influential factors — total license plate quota, vehicle rental fee, and energy price — on EV adoption. The LPC policy under COVID-19 is also studied through a scenario analysis. If the government additionally increases the total quota by 20,000, 24% could be allocated to GV and 76% to EV. One third reduction of the current vehicle rental fee could increase EV license plates by 10.5%. In terms of energy prices, when gasoline price is low, reducing electricity prices could contribute to EV adoption significantly, while that effect tapers off as gasoline prices increase. • The optimal license plate allocation between EVs and GVs in Beijing is analyzed. • A Stackelberg game is proposed to model stakeholders' interaction under LPC policy. • The optimal license plate allocation policy under COVID-19 is also studied. • One third reduction of vehicle rental fee could increase EV adoption by 10.5%. [ABSTRACT FROM AUTHOR]

Blind search for available parking space is accountable for most traffic congestion, accident, and pollution in cities, which severely impact people's life. Parking management based on an online smart parking system is practical to alleviate parking problems in which parking allocation is the core. However, existing researches are weak at satisfying allocation effect and speed simultaneously when solving large-scale dynamic parking allocation problem. To address this problem, we firstly construct an online "Collection-Allocation-Response" smart parking system (CARSP) to offer parking services to users and rent parking spaces from owners so as to obtain revenue for system managers. We then propose a novel Doubly Periodic Rolling Horizon allocation approach (DPRH) that circularly conduct allocation within a short period and reallocation within a long period. We formulate a narrow allocation model (without reallocation) and broad allocation model (with reallocation), both of which are binary integer programming models with the objective of maximizing system integrated benefit. We design seven performance metrics to evaluate the overall allocation effect and speed of CARSP based on DPRH. According to the three-day district-level instance in Beijing, CARSP based on DPRH performs excellently in balancing allocation effect and speed. This study is meaningful for constructing and optimizing an online smart parking system. [ABSTRACT FROM AUTHOR]

• Bayesian inference and deep learning is integrated for traffic congestion modeling; • A dynamic Bayesian graph convolutional network (DBGCN) is proposed. • The DBGCN outperforms the state-of-the-art prediction models. • The DBGCN can simulate congestion evolution via dynamic adjacency matrix. • The change of congestion source location leads to different congestion patterns. Congestion, whether recurrent or non-recurrent, propagates through the road network. The process of congestion propagation from a particular road to its neighbors can be regarded as a kind of message passing with a directed relationship. Existing methods have created a solid foundation for characterizing congestion propagation; however, they are either built upon simplified assumptions in traffic flow theory or predefined relationships among road sections, which would lead to downgraded accuracy in practice. This paper proposes a dynamic Bayesian graph convolutional network (DBGCN), which integrates Bayesian inference into a deep learning framework. Therefore, the rules of congestion propagation in the network can be actively learned from the observed data instead of predefining them based on prior knowledge. Experimental results on 971 testbeds in a regional road network in Beijing demonstrate that DBGCN outperforms the state-of-the-art models in inferring the congestion propagation spatiotemporal coverage and reveals variations in congestion propagation patterns according to the road network structure. Furthermore, the proposed model can simulate the congestion propagation process in customized scenarios by learning the latent congestion propagation rules. The results in different scenarios show that the change of congestion source location leads to distinct congestion magnitude, and the propagation of congestion will eventually stop at the road sections with strong shunting effect. [ABSTRACT FROM AUTHOR]

To quantify the responses of drivers to traffic information and the congestion evacuation effect on the basis of traffic guidance information, a multilayer network congestion risk propagation model of urban roads was built to analyse the influence of the advanced traveller information system (ATIS) penetration rate, group behaviours of drivers, and traveller flow distribution features on the traffic congestion risk propagation of urban roads. Meanwhile, the dynamic evolutionary characteristics of group behaviours of drivers in a road network under the guidance of traffic information were analysed with the microscopic Markov chain approach (MMCA). A simulation analysis of the artery network in the fourth ring of Beijing was also carried out. Results demonstrated that the influence of traffic information and drivers' information reinforcement psychology on congestion risk propagation depend on the aggregation effect caused by traffic information. Increasing the ATIS market penetration rate and drivers' acceptance of information is beneficial to relieve traffic congestion as long as the drivers' aggregation effect is within a critical range. [ABSTRACT FROM AUTHOR]

Travel time prediction is playing an increasingly important part in advanced traveler information system (ATIS), which is of great significance to alleviate urban traffic congestion. Although graph convolutional networks have been widely used in road network traffic prediction, spatiotemporal dynamic modeling of urban traffic is still an intractable task. In this study, we propose an improved graph convolutional network (IGC-Net) for travel time prediction. Specifically, we design a modified adjacency matrix by fusing distance and correlation matrix with original adjacency matrix to capture spatial dynamic feature. We then establish three components based on temporal property to capture recent, daily-periodic, and weekly periodic correlations. The comparison experiments with baseline models and variants on a real-world dataset in Beijing are conducted. The results show that the IGC-Net outperforms baseline models in different prediction horizons and has stronger robustness for dynamic traffic prediction. [ABSTRACT FROM AUTHOR]

This study aims to examine the effects of moving work-zone speed, working lane position, and traffic condition on the travel delay, crash risk, and mortality risk at moving work zones by using microscopic traffic simulation approaches. Two surrogate safety measures including TTC (Time to Collision) and Delta-V are employed to represent the vehicle crash risk and mortality risk, respectively. The simulation experiment is conducted based on the observation data from the road network located in the Haidian District of Beijing. Simulation results show that the moving work-zone speed exhibits higher effects on the travel delay, vehicle crash risk, and mortality risk under the smooth traffic state, as compared with those under the congested traffic state. Greater travel delay and higher crash risk are closely associated with the lanes where there are a larger proportion of vehicles whereas the highest mortality risk is for vehicles traveling in the fast lane. The simulation results also show that the vehicles near the moving work zone are associated with high vehicle crash risk, whereas an increased crash risk is also found for the vehicles far from the moving work zone. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between tourists' information demand about entrance tourist flow control schemes (ETFCS) and tourists' entrance transfer decision-making behavior, using the case study of the Summer Palace, one of the most important resort destinations in Beijing, China. After discussing tourists' decision-making behavior under ETFCS information, a revealed preference and stated preference survey (N = 1742 valid observations) were created and a joint choice model of tourist's entrance transfer and diversion entrance selection was established. Results show how various ETFCS are formulated and affect tourists' decision-making behavior under different traffic congestion. Sensitivity analysis reveals that tourists' entrance transfer decision-making behavior is related to the entry time of current crowded entrance and the time saved from the diversion entrance. Hence it is possible to formulate ETFCS in terms of tourist's spatial-temporal distribution in order to anticipate the levels of tourist intensity, especially during peak periods. [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]

As a market-based instrument of transportation demand management, congestion charge can not only effectively reduce traffic congestion, but also improve air quality. However, due to its low public acceptability, this policy only has a few urban practices. As one of the fast-growing metropolises in emerging economies that are facing both traffic congestion and industrial pollution problems, Beijing is now considering the feasibility of implementing congestion charging. Some researchers address that though people with strong environmental concerns are more prone to support congestion charges, the associations between environmental concerns and support for congestion charges are context-dependent. A survey was conducted in Beijing in 2016 to understand how the pollution context in cities of emerging economies affects these associations. We find that the acceptability in Beijing is 33%, and expected policy effects and environmental concerns are the most important impact factors. Due to the influence of the regional industrial pollution context, most residents in Beijing do not consider congestion charge to be an effective way to tackle air pollution. Under these circumstances, even if the public environmental concerns, in general, are high and congestion charges are "marketed" as environmental policies, there is no guarantee that policy support will rise. [ABSTRACT FROM AUTHOR]

Hospital accessibility is a vital factor in identifying areas with a shortage of healthcare. Due to a lack of data, however, few measures of hospital accessibility in existing studies have considered individual preferences for certain hospitals. To address this issue, we propose the concept of "observed hospital accessibility" (OHA) incorporating trips to the hospital by taxi in Beijing. A comparative analysis was conducted between OHA and the potential hospital accessibility (PHA) method adopted in previous studies. The results suggest that PHA overestimates the accessibility of hospitals in the periphery of the city center where there is more traffic congestion, but underestimates accessibility in the suburbs, as suburban healthcare needs can be met by nearby hospitals. In summary, the OHA method offers a novel approach to employing trip records in the investigation of hospital access behavior when the availability of patient register data is limited. It also provides insights for other activity-based travel behavior studies. • This study first proposed the concept of observed hospital accessibility (OHA) and conducted an enhanced 2SFCA method with taxi trip data for evaluation. • OHA method offers a novel approach to employ trip records for researching out-patient treatment activities. • OHA spatially fitted a polycentric structure, whereas potential hospital accessibility (PHA) showed a monocentric structure with the case study of Beijing. • The paper provides insights for other activity-based travel behavior study. [ABSTRACT FROM AUTHOR]