Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters’ route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.

Road network traffic management and control are the key mechanisms to alleviate urban traffic congestion. With this study, we aimed to characterize the traffic flow state of urban road networks using the Macroscopic Fundamental Diagram (MFD) to support area traffic control. The core property of an MFD is that the network flow is maximized when network traffic stays at an optimal accumulation state. The property can be used to optimize the temporal and spatial distribution of traffic flow with applications such as gating control. MFD construction is the basis of these MFD-based applications. Although many studies have been conducted to construct MFDs, few studies are dedicated to improving the accuracy considering the reliability of different sources of data. To this end, we propose an MFD construction method using multi-source data based on Dempster–Shafer evidence (DS evidence) theory considering the reliability of different data sources. First, the MFD was constructed using VTD and CSD, separately. Then, the fused MFD was derived by quantifying the reliability of different sources of data for each MFD parameter based on DS evidence theory. The results under real data and simulated data show that the accuracy of the constructed MFDs was greatly improved considering the reliability of different data sources (the maximum MFD estimation error was reduced by 22.3%). The proposed method has the potential to support the evaluation of traffic operations and the optimization of signal control schemes for urban traffic networks.

The macroscopic fundamental diagram (MFD), as a model depicting the correlation between traffic flow parameters at the network level, offers a new way to understand regional traffic state using derived traffic flow data from detectors directly. The accuracy of MFD construction is directly related to factors such as the type of detectors, their distribution, and their quantity within the road network. Understanding these influencing factors and mechanisms is crucial for enhancing the reliability of MFD-based applications such as congestion pricing and threshold control. Present investigations on factors that affect MFD construction’s accuracy have frequently been confined to sensitivity analysis of single-source data and individual influencing factors such as the penetration rate. However, the accuracy of MFD is influenced by a multitude of factors, including the spatial distribution equilibrium, penetration rate, and coverage rate of traffic flow detection equipment. Despite this, this paper utilized the Q-paramics simulation software V6.8.1 to acquire simulated data and employed the orthogonal experimental method from statistics to explore the impact mechanisms of factors on the accuracy of MFD construction. The results of the case study demonstrated that when the penetration rate reaches 20%, the error remains approximately around 10%; once the coverage rate surpasses 45%, the errors stabilize at around 10%. This study provides practical guidance for traffic management and planning decisions aimed at promoting sustainable development through the application of MFD in real-world road networks.

Urban congestion is difficult to alleviate through road expansion alone, making it necessary to improve existing road-network performance while maintaining public-transport priority. Under transit-priority policies, buses and private vehicles share limited road space, and bus lanes, stops, and lane-changing interactions may either improve or reduce network efficiency. This study treats capacity improvement as a constrained network-performance objective that preserves feasible bus operation and supports higher-occupancy, lower-emission public transport. A simulation-based capacity evaluation framework was developed using the three-dimensional macroscopic fundamental diagram (3D-MFD). A grid network was built in Simulation of Urban Mobility (SUMO), and 16 scenarios were designed by varying four factors: dedicated bus-lane proportion, average bus dwell time, driver lane-changing willingness, and bus-to-private-vehicle ratio. The 3D-MFD was fitted by nonlinear least squares, and range analysis and analysis of variance were used to identify significant factors. Results show that bus-lane proportion and bus-to-private-vehicle ratio dominate capacity variation. A supplementary simulation-based transferability assessment on a Beijing subnetwork further showed that the 4% bus-lane share and 7% bus-to-private-vehicle ratio produced the highest tested capacity response. The findings provide an assumption-bounded basis for screening bus-priority parameters rather than universal design values.