An insufficient functional relationship between adjustment factors and saturation flow rate (SFR) in the U.S. Highway Capacity Manual (HCM) method increases an additional prediction bias. The error of SFR predictions can reach 8–10%. To solve this problem, this paper proposes a comprehensive adjusted method that considers the effects of interactions between factors. Based on the data from 35 through lanes in Beijing and 25 shared through and left-turn lanes in Washington, DC, the interactions between lane width and percentage of heavy vehicles and proportion of left-turning vehicles were analyzed. Two comprehensive adjustment factor models were established and tested. The mean absolute percentage error (MAPE) of model 1 (considering the interaction between lane width and percentage of heavy vehicles) was 4.89% smaller than the MAPE of Chinese National Standard method (Standard Number is GB50647) at 13.64%. The MAPE of model 2 (considering the interaction between lane width and proportion of left-turning vehicles was 33.16% smaller than the MAPE of HCM method at 14.56%. This method could improve the accuracy of SFR prediction, provide support for traffic operation measures, alleviate the traffic congestion, and improve sustainable development of cities.

Intersections are the bottlenecks of the road network. The capacity of signalized intersections restricts the operation of the road network. Dynamic estimation of capacity is necessary for signalized intersections refined management. With the development of technology, more and more detectors were installed near the intersection. It had been the information-rich environment, which provided support for dynamic estimation of capacity. A dynamic estimation method for a saturation flow rate based on a neural network was developed. It would grasp the dynamic change of saturation flow rates and influencing factors. The measure data at three scenarios (through lanes, shared right-turn and through lanes, shared left-turn and through lanes) of signalized intersections in Beijing were taken as examples to validate the proposed method. Firstly, the traffic flow characteristics of the three scenarios and factors affecting the saturation flow rate were analyzed. Secondly, neural network models of the three scenarios were established. Then the hyperparameters of neural network models were determined. After training, the neural network structure and parameters were saved. Lastly, the test set data was validated by the training model. At the same time, the proposed method was compared with the Highway Capacity Manual (HCM) method and the statistical regression method. The results show that both regression models and neural network models have better accuracy than HCM models. In a simple scenario, the neural network models are not much different from the regression models. With the increase of complexity of scenarios, the advantages of neural network models are highlighted. In through-left lane and through-right lane scenarios, the estimated saturation flow rates used by the proposed method were 7.02%, 4.70%, respectively. In the complexity of traffic scenarios, the proposed method can estimate the saturation flow rate accurately and timely. The results could be used for signal control schemes optimizing and operation managing at signalized intersections subtly.

The geometric design of airport curbsides is critical for both airport planners and passengers as it affects airport ground traffic capacity and creates airline delays. Rational design of curbsides can yield increased efficiency in the use of road space and time resources. Yet a limited number of studies have reported the impact of traffic features on curbside’s geometric design. This paper provides a two-lane curbside measurement with a simulation model to calculate the length of curbsides, which solves the curbside berth supply and curbside length calculation problems using a refined curbside parking model (RCPM) with constraints of a car-following (CF) model, as well as uniformly distributed speed, dwell time, and parking demand; it thereby unveils the detailed microscopic mechanisms of interaction of traffic features and curbsides’ length. The required lengths of curbsides under various parking demands are obtained after a set of numerical simulations. The result further reveals the significant impacts of car-following and braking behaviors, particularly under the huge parking demands on curbside traffic flow. It also demonstrates that a certain delay is created and a suboptimum level of service (LOS) occurs even though satisfying all parking demands, which indicates that lengthening the curbside and increasing parking supply do not necessarily improve the LOS.

Shared parking has been widely accepted as an effective way to deal with the mismatch between parking demand and supply. Especially for adjacent construction areas with mixed residential and commercial land, staggered shared parking has broad application prospects. From the previous practice, the public welfare from the government, the commercial interests of parking enterprises, and the individual income of residents will be the key elements to promote a staggered parking policy in adjacent construction areas. However, the current research on shared parking mainly focused on the commercial factors to improve the operating efficiency and operating benefit for parking enterprises; there is a lack of research on the implementation process of staggered parking policies which will solve residential areas’ parking problem with the interests of residents as the core. Here, this paper focuses on exploring residents’ and businesses’ intention to participate in the process of a staggered shared parking policy, where businesses have certain location and condition qualifications. Firstly, this study used two extended theoretical models of planned behavior to explore the behavioral intentions of users and providers in this staggered shared parking event, respectively. Secondly, the research hypothesis was examined using a structural equation approach, and a questionnaire was designed to survey 323 residents and 282 enterprises in the core urban area of Beijing. Ultimately, the study indicated that, for residents, perceptual behavior control has the greatest impact on the perceived intention, while the perceived ease of use and perceived usefulness play a crucial influential role in the willingness to use staggered shared parking. For companies, attitude has the strongest impact on the perceived intention. Our findings reveal the intrinsic impact mechanism of the policies in the decision-making process, contributing to the precise policy implementation to alleviate the problem of difficult parking for residents, thus improving the city’s parking management.

During the highway reconstruction and expansion, some lanes are often closed on the construction side to ensure that the construction is carried out normally. The presence of the work zone increases the traffic pressure on the construction side of the highway, causing traffic congestion, increased $C{O}_2$ emissions from motor vehicles, and increasing environmental pollution. The bi-level programming model was developed based on the objective of minimizing the travel time and total $C{O}_2$ emissions of the system so as to solve it using a quantum particle swarm algorithm with high convergence speed and high intelligence to form the lane optimization scheme for the three forms of reclosing and expanding six-lane highways in both directions. The results show that reasonable use of opposite non-construction lanes in the work zone of a partially closed highway expansion can reduce the total system travel cost, alleviate traffic congestion, reduce $C{O}_2$ emissions, and contribute to the sustainable development of transportation, as well as the environment.