With increased highway mileage, various types and quantities of infrastructure are equipped on the roadside to improve traffic safety and efficiency but also encounter difficulty in asset management. The collected data are separately stored with diverse formats, granularity and quality, causing repeated acquisitions and islands of information coherence. The life-cycle interoperability of infrastructure data are required to support life-cycle application scenarios in sustainable development. This paper analyzes 459 papers and 538 survey questionnaires to obtain the literature and practical digital requirements, including unified classification and standardized formats, linkage from separated data sources, support for data analysis across different scenarios, etc. To satisfy these requirements, an infrastructure digitalization framework is proposed, including road infrastructure and other data, data governance, life-cycle data integration, application scenarios, regulations and standards, and performance assessment. The application scenarios involve four categories—design and construction, maintenance, operation, and highway administration—each of which contains four or five scenarios. Then, the data integration approach is first developed with master data identification and determination of data elements for data interoperation between different application scenarios, using a modified data–process matrix, correlation matrix, and evaluation factors. A data relationship model is adopted to present static and dynamic correlations from the multi-source data. Numerical experiments are implemented with two practical highway administration and maintenance systems to demonstrate the effectiveness of the data integration approach. Master data identification and data element determination are applied to guide life-cycle data interoperation.

The quality of traffic information has become one of the most important factors that can affect the distribution of urban and highway traffic flow by changing the travel route, transportation mode, and travel time of travelers and trips. Past research has revealed traveler behavior when traffic information is provided. This paper summarizes the related study achievements from a survey conducted in the Beijing area with a specially designed questionnaire considering traffic conditions and the provision of traffic information services. With the survey data, a Logit model is estimated, and the results indicate that travel time can be considered the most significant factor that affects highway travel mode choice between private vehicles and public transit, whereas trip purpose is the least significant factor for private vehicle usage for both urban and highway travel.

Within the physical structure of the highway network, the key segments are significant for both the resistance against emergency and operational efficiency. Based on the Graph Theory and Network Theory, this paper proposes a new method for identifying key nodes in highway networks. First, a benefit function was estimated for each node with weights of GDP, mileage, and capacity. A benefit indicator was calculated, which represented the partial importance of each node. Second, weighted betweenness was estimated for describing the general importance throughout the whole network. After that, the overall importance for each node was calculated by finding weighted averages for both partial and general importance. Finally, a verification of the new method was conducted with a highway network among five eastern provinces and cities in China. The results indicated that the overall importance estimated by this method matched with the reality.

Using the Network Theory, comparisons were made with node degree distributions, betweennesses, shortest path lengths, and clustering coefficients of the provincial Expressway network and Trunk Highway network consisting partly of the Expressway System and the National Highway System in the same province. Based on structural characteristics of networks, this paper focused on comparing vulnerabilities between the provincial Expressway and Trunk Highway network. In addition, destructive experiments focusing on edge betweenness were conducted, while reactions and reliabilities from the Expressway Network and Trunk Highway Network were observed. Results showed that when 15% of all segments were damaged or blocked, the general efficiency of the Expressway System reduced to half of normal, which meant the whole system almost stopped running. On the other hand, this situation would occur when 25% of segments were damaged or blocked in the Trunk Highway Network, and this indicated the Trunk Highway Network had higher reliability than the Expressway System.

As the next generation of information service develops, its role in determining the future of transportation information service systems is becoming more and more significant. Service dissemination methods of traffic information are transferring from Websites, calling centers, roadside VMS, and other traditional approaches to cellphones, PADs, and other Mobile Internet devices. This paper analyzed the increasing demand for traffic information service in typical urban areas through the characteristics of economic, demographic, vehicle ownership, and trip feature in the city of Beijing, China. In addition, the significant increasing trend of Mobile Internet device popularizing rate was investigated with respect to Mobile Internet devices, Mobile Internet users, and mobile applications (APPs). Results indicated that personalized traffic information services based on Mobile Internet devices were following an explosive growth trend. Also, it was believed that the mobile APP was going to be the core approach in the future traffic information service system.

Traffic state identification is an important part in real-time traffic management and intelligent transportation systems (ITS) applications, which is conventionally based on the available single dataset from traffic sensors such as loop detector, microwave detector, video camera, Bluetooth detector, etc. An accurate profile of the traffic state on highway corridor in terms of speed and density is expected to be a critical factor for supporting traffic management measures. Data fusion techniques are nowadays applied to ITS to improve efficiency and safety of highways. As more and more ETC and DSRC data can be obtained, a method of analyzing traffic status with the datasets gained from ETC via DSRC is expected. In this paper, the DSRC was firstly large scaled installed and applied as a traffic detector along Jingcheng Highway, and the data from DSRC based detector and tolling data were used to identify the traffic state along this corridor. A data fusion method was developed in order to consider both space and time effects was proposed. The results indicated that the DSRC detector could be a promising way to collect traffic data and represent the traffic state on the network with ETC data.