Road network patterns can affect traffic performance, travel behavior, and traffic safety. Thus, a deep understanding of the properties of different network patterns can provide useful guidance for design and improvement of road systems. The aim of this study is to build a relationship between graphical and topological features of road network patterns of traffic analysis zones (TAZ) and, on the basis of this relationship, to offer a measure that can quantitatively distinguish different graphical pattern types. Toward this goal, a topological analysis measure, centrality, is applied to investigate road network patterns metrically at the TAZ level. First, 662 TAZ road networks are classified according to the graphical features of the networks; then different graphical features are calculated for centrality indices including network degree centrality, network betweenness centrality, and network closeness centrality. It is concluded that the network betweenness centrality is the best measure to distinguish and describe various TAZ road network patterns. Finally, the problem of how to assign a road that happens to be on the border of two adjacent TAZs is studied. A measure that can quantitatively describe and represent different road network patterns is offered. This measure could be useful for further evaluation of the possible effects of TAZ road network patterns on transportation.

In order to better predict the traffic states on freeways and make management decisions, a hybrid model of support vector machine (SVM) and K-nearest neighbor (KNN) is proposed for short-term freeway exiting volume prediction. First, a historical data set is built by using the freeway toll data. The abnormal toll records, such as records that have same entry and exit station, illogical time record and abnormal travel speed, are excluded by data quality control. Based on the historical dataset, it is found that the exiting volume has periodical variation over time which provides the basis of the short-term prediction. Then, the historical data set is cross-classified into twelves groups based on the day of week and time of day. The prediction has been done for each group. Finally, the prediction is accomplished by the hybrid-model of SVM and KNN. The exiting volumes of previous time periods are used as the feature vector for KNN and SVM. Besides, a dynamic weight is adopted for the prediction of current ti...

Microscopic vehicle emissions models have been well developed in the past decades. Those models require second-by-second vehicle trajectory data as a key input to perform vehicle energy/emissions estimation. Due to the omnipresence of mobile sensors such as floating cars, real-world vehicle trajectory data can be collected in a large scale. However, most large-scaled mobile sensor data in practice are sparse in terms of sampling rate due to the consideration in implementation cost. In this paper, a new modal activity framework for vehicle energy/emissions estimation using sparse mobile sensor data is presented. The valid vehicle dynamic states are identified including four driving modes, named acceleration, deceleration, cruising, and idling. The best valid vehicle dynamic state with the largest probabilities is selected to reconstruct the second-by-second vehicle trajectory between consecutive sampling times. Then vehicle energy/emissions factors are estimated based on operating mode distributions. The proposed model is calibrated and validated using the Next Generation Simulation's dataset, and shows better performance in vehicle energy/emissions estimation compared with the linear interpolation model. Sensitivity analysis is performed to show the model accuracy with different time intervals. This paper provides a new methodology for vehicle energy/emissions estimation and extends the application area of sparse mobile sensor data.

This studyutilized the mobile signaling data to conductthe impact analysis of jobs-housing spatial mismatch on commuting behavior, with eight typical employment centers of three categories selected as the research subjects. Based on the analysis of the characteristics and indictors including commuting distance, accessibilities from cumulative opportunity model etc., this study demonstrates that (a) cumulative percentage of short commuting distance (e.g., less than 3 km) reflects the jobs-housing spatial match between employment centers and their peripheral areas; and (b) combining the indicators of employed population and area covered within a certain space-time range among indictors of accessibility, it is possible to identify the degree of jobs-housing balance and efficiency of the transport system. According to the evaluation radar maps, the authors believe that employment centers could be divided into three categories: those with a gathering power, those with improvable functions, and those with local adjustment potentials. Possible measures including controlling the gathering power of the city centers, improving the function mix and transport facilities, and optimizing the overall local environment, etc. could be made to achieve jobs-housing balance in central districts and their peripheral areas as a whole. Besides, the study, proceeding from the perspective of commuters, suggests that optimization of jobs-housing distribution along banded corridors would be more efficient than those within the traditional region so as to reduce commuting traffic load.

Urban arterials in Shanghai usually have high intersection densities, which can lead to frequent and severe congestion. Research in this area of study focuses on urban arterial operating speed and its relationship with road geometry, traffic control, and traffic volume, and more study is needed before remedies to the congestion problems can be identified. In Shanghai, an opportunity exists to investigate urban arterial operating speed by accessing continuously updated global positioning system (GPS) data from taxis, hereafter called floating car data (FCD). With more than 50,000 taxis equipped with GPS running in the Shanghai road network, it is feasible to acquire comprehensive, citywide arterial speed measurements throughout the network. In this study, 45 arterials in Shanghai, which comprised of 177 two-direction segments bounded by signalized intersections, were selected for study. GPS data from taxis were captured during peak and off-peak hours to enable the calculation of the mean operating speed for each selected segment. Information of road geometry, traffic control, and traffic volume data was also acquired to examine their influence on operating speed. Multilevel linear regression models were utilized to analyze operating speed with the arterial level’s unobserved heterogeneity accounted for as well. It was identified that roadway design variables, including segment length, number of lanes, and horizontal degree of curvature, left-turn lane types, and presence of median along with saturation degree, are all significantly related to the operating speed. The results of this study shed some light on roadway design, roadway improvement, and traffic management.

Bus emissions have become one of the important contributing factors in urban environmental pollution due to the frequent use of heavy-duty diesel engines in the day-time. Local bus driving cycles have a significant influence on bus emissions under the different traffic conditions. This study investigated the operation mode distributions and emission characteristics for urban buses based on localized MOtor Vehicle Emission Simulator (MOVES) using sparse Global Position System (GPS) data in Shanghai, China. Sparse GPS data from forty-three buses were prepared, and then bus trajectories were reconstructed to calculate local bus driving cycles, including model description, model calibration, and trajectory reconstruction. MOVES localization was conducted for emission estimation mainly focusing on the bus emission inventory comparison between US and China. Bus emission factors were estimated based on the localized MOVES from the aspect of different driving conditions. Results show that with the increase in average traveling speed, the proportion of idling operation mode showed a decreasing trend. Four typical vehicle operation mode distributions were identified with different average speeds to show the impact of traffic conditions. Bus emission factors first rapidly decreased and then slowly declined towards some minimum values. Bus lanes exhibited emission reduction benefits under serious traffic congestion. The findings of this study have great importance for transportation operation management and policy-making to reduce bus emissions, as well as improving air quality.