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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]

The rapid pace of urbanization and population growth has led to heightened traffic congestion and extended commuting distances, consequently amplifying uncertainties in travel times. For decades, studies have employed average or median travel time to assess job accessibility while neglecting variations and uncertainty in travel time, ultimately leading to imprecise evaluations of urban job accessibility. To bridge this gap, this study investigates the temporal fluctuations in job accessibility with the Accessibility Buffer Index (ABI), which measures the proportionate reduction in accessibility when the transport system fails to operate optimally. Gathering speed data at the street level in Beijing from January to June 2022, we utilize the cumulative-opportunity model with three distinct travel budgets (15 min, 30 min, and 45 min) to finely estimate daily job accessibility in spatiotemporal detail. Our findings reveal significant spatial heterogeneity in job accessibility and ABI, highlighting notable disparities in their spatial distribution. Through the use of ABI, our study offers valuable empirical evidence regarding the characteristics of accessibility fluctuations, thereby presenting a novel perspective to enhance sustainable and reliable urban transportation. • We investigate the temporal fluctuations in job accessibility with the Accessibility Buffer Index (ABI). • The ABI measures the proportionate reduction in accessibility when the transport system fails to operate optimally. • We utilize the cumulative-opportunity model to estimate daily job accessibility. • We found significant spatial heterogeneity in job accessibility and the ABI. • The study highlights notable disparities in the spatial distributions of job accessibility and the ABI. [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]

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers' departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers' unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules. [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]

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]

Assessing and prioritizing the duration time and effects of traffic incidents on major roads present significant challenges for road network managers. This study examines the effect of numerous factors associated with various types of incidents on their duration and proposes an incident duration prediction model. Several parametric accelerated failure time hazard-based models were examined, including Weibull, log-logistic, log-normal, and generalized gamma, as well as all models with gamma heterogeneity and flexible parametric hazard-based models with freedomranging fromone to ten, by analyzing a traffic incident dataset obtained from the Incident Reporting and Dispatching System in Beijing in 2008. Results show that different factors significantly affect different incident time phases, whose best distributions were diverse. Given the best hazard-based models of each incident time phase, the prediction result can be reasonable for most incidents. The results of this study can aid traffic incident management agencies not only in implementing strategies that would reduce incident duration, and thus reduce congestion, secondary incidents, and the associated human and economic losses, but also in effectively predicting incident duration time. [ABSTRACT FROM AUTHOR]

This study aimed to analyze traffic congestion in urban road networks. The speed performance index was adopted to evaluate the existing road network conditions of congestion, then road segment and network congestion indexes were introduced to respectively measure the congestion levels of urban road segment and network. This study also carried out a traffic congestion analysis for Beijing expressway network, based on the speed performance data collected from January 1 to November 1, 2012, by Beijing Traffic Management Bureau (BTMB). Based on these analyses the proposed congestion indexes can well assess the traffic congestion conditions of urban road networks, more importantly, such an assessment study provides traffic control and management agencies an accurate and clear understanding of operation status of traffic networks. [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]

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]

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]

Urban transportation problems, primarily characterized by traffic congestion and exhaust emissions, continue to worsen, significantly impeding the economic and social development of cities. Road pricing strategies have emerged as an effective approach to tackle urban transportation problems, and their successful implementation in numerous cities have garnered significant attention. In this context, this study introduces a collaborative road pricing strategy tailored for mixed traffic flow comprising both conventional vehicles (CVs) and new energy vehicles (NEVs). CVs and NEVs are considered as two distinct types of cars with heterogeneous characteristics. The collaborative road pricing strategy consists of two components: congestion pricing and emission pricing. A bi-level programming model for the collaborative road pricing strategy is built. Exhaust emissions within the low-emission zone (LEZ) are used as constraints for the model, while the optimization objective of the model involves minimizing the sum of total travel time and total emissions of the road network. The viability of the proposed collaborative road pricing scheme is demonstrated through numerical tests within the second ring road in Beijing, China. The results imply that the service level of the road network can be improved by implementing the collaborative road pricing scheme. It also concludes that, increasing the penetration of new energy vehicles would help to decrease traffic congestion and emissions. [Display omitted] • A collaborative road pricing scheme for heterogenous vehicles is formulated. • Heterogenous vehicles include conventional vehicles and new energy vehicles (NEVs). • The low-emission zone (LEZ) is districted as the emission constraint in the scheme. • The influence of the NEV penetration and the LEZ emission constraint is examined. • The penetration of NEVs would help to decrease traffic congestion and emissions. [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]

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]

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]

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]

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]