Urban road intersections play an important role in deciding the total travel time and the overall travel efficiency. In this paper, an innovative traffic grid model has been proposed, which evaluates and diagnoses the traffic status and the time delay at intersections across whole urban road networks. This method is grounded on a massive amount of floating car data sampled at a rate of 3 s, and it is composed of three major parts. (1) A grid model is built to transform intersections into discrete cells, and the floating car data are matched to the grids through a simple assignment process. (2) Based on the grid model, a set of key traffic parameters (e.g., the total time delay of all the directions of the intersection and the average speed of each direction) is derived. (3) Using these parameters, intersections are evaluated and the ones with the longest traffic delays are identified. The obtained intersections are further examined in terms of the traffic flow ratio and the green time ratio as well as the difference between these two variables. Using the central area of Beijing as the case study, the potential and feasibility of the proposed method are demonstrated and the unreasonable signal timing phases are detected. The developed method can be easily transferred to other cities, making it a useful and practical tool for traffic managers to evaluate and diagnose urban signal intersections as well as to design optimal measures for reducing traffic delay and increase operation efficiency at the intersections.

DRT service, designed to be flexible in time and space, follows the contemporary trend of on-demand transit provision. However, this type of service often suffers from low profitability due to small demand and/or high operation costs. DRT service is a local business in nature. The existing research primarily focuses on DRT service for regions with low transit demand, but it does not take into account service operation for other types of regions. This study aims to fill in this gap and identify the sources of benefit from DRT operations in varied types of regions. To this end, the analysis compares the DRT operation performance between overpopulated heartland and underpopulated hinterland regions; in each region, the benefit is identified through the difference in key performance indices between the simulated DRT and actual bus operation. The data on the road network and bus operation in Daegu, Korea, in 2021 are used for the DRT simulation. The results show that the heartland DRT benefits more from the reduced vehicle kilometers, while the hinterland DRT gains mostly from the reduced waiting time. Given that both DRT types outperform existing bus services, it is revealed that the heartland DRT is more reliable than the hinterland DRT due to the nature of regional characteristics.

On-ramps are considered to be one of the common traffic bottlenecks. In order to improve the operation efficiency of on-ramps, scholars worldwide have proposed various vehicle merging strategies. In this study, we designed different rules to express three collaborative strategies and studied their impact on on-ramp systems. Cellular automata models were used to simulate the systems under different situations, and the average speed and traffic flow rate of both the main roads and ramps were analyzed. The results show that (1) all the three merging strategies give excessive “priority” to the merging vehicle, leading to a severe reduction in the traffic performance of the main road; (2) nevertheless, these strategies have different effects on the entire system with a one-lane or two-lane main road. Due to the lane-changing behavior, the system with a two-lane main road has more advantages than that featured with a one-lane road, making the former system performing better than the latter under the same strategies; (3) the vehicles on the ramp and main road affect each other, and as the vehicle entering probabilities become large, the traffic flow rate on the main road decreases whereas that on the ramp increases. However, the effect is not unlimited, the flow rate on both roads finally reaches a stable level (forming a “platform”); and (4) large values of the merging safety distance parameter decrease the flow rate of the entire system. All the previous results provide a deep understanding of the impact of the three merging strategies on traffic flow, contributing to the design of on-ramp systems that have better operation efficiency and low levels of congestion.

In-home activities are inevitably important parts of individuals’ daily schedules, as people spend more time working and doing various other activities (e.g., online shopping or banking) at home. However, conventional activity-based travel demand models (ABMs) only consider travel and travel-related out-of-home activities, ignoring the interaction between in-home and out-of-home activities. To fill in this gap and increase the understanding of what people do at home and how in-home and out-of-home activities affect each other, a new method is proposed in this study. The approach predicts the types and durations of in-home activities of daily schedules generated by ABMs. In model building, statistical methods such as multinomial logit, log-linear regression, and activity sequential information are utilized, while in calibration, the Simultaneous Perturbation Stochastic Approximation (SPSA) method is employed. The proposed method was tested using training data and by applying the approach to the schedules of 6.3 million people in the Flemish region of Belgium generated by a representative ABM. Based on the statistical methods, the mean absolute errors were 0.36 and 0.21 for predicting the number and sum of the durations of in-home activities (over all types) per schedule, respectively. The prediction obtained a 10% and 8% improvement using sequential information. After calibration, an additional 60% and 68% were gained regarding activity participation rates and time spent per day. The experimental results demonstrate the potential and practical ability of the proposed method for the incorporation of in-home activities in activity–travel schedules, contributing towards the extension of ABMs to a wide range of applications that are associated with individuals’ in-home activities (e.g., the appropriate evaluation of energy consumption and carbon emission estimation as well as sustainable policy designs for telecommuting).