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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]

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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]

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]

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]