• Energy Research

Accelerated Bridge Construction (ABC) is bridge construction that uses innovative planning, design and construction methods in a safe and cost-effective manner, which reduces construction mobility and environmental impacts, and contributes to city sustainable planning and development. To deal with the pressing need to support the decisions associated with the selection between the ABC and conventional bridge construction, this paper presents the development of a multi-criteria evaluation framework. Methods are developed and identified to estimate the construction, agency, and user costs associated with the construction methods. A novel model was developed to allow the estimation of the construction and agency costs of ABC relative to conventional construction. This paper also demonstrates the estimation of user costs, including those associated with mobility, reliability, safety, and emissions, utilizing combinations of the proposed prediction method. The paper then compares the use of the return-on-investment and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) Multi-Criteria Decision Making (MCDM) evaluation approaches in the decision to select between ABC and conventional bridge construction. The results from the employment of the two approaches to a case study demonstrate the advantage of using the TOPSIS approach, which is also applicable in the urban planning process.

Adoption of electric vehicles (EVs) has been regarded as one of the most important strategies to address the issues of energy dependence and greenhouse effect. Empirical reviews demonstrate that wide acceptance of EV is still difficult to achieve. This research proposes to investigate the factors that might trigger the wide usage of EVs to support the energy policy. The real-world owners of EV were extracted from the 2017 National Household Travel Survey (NHTS), which provides large-scale individual characteristics. NHTS dataset was processed to establish the comprehensive estimation model for EV adoption with considering vehicle, personal and household factors. Besides the commonly social-economic factors, the gasoline price and car sharing program were found to be significant for EV adoption. Additionally, since the EV owners are only 1.29% of all vehicle owners, this article introduced the imbalanced dataset technique, which was seldom considered in existing researches. Subsequently, several machine learning methods were utilized to build the prediction model, and the model performance analysis indicates the Decision Tree (DT) model outperforms other models. A regional EV penetration map was also generated for the U.S. to validate the proposed approach. Implications for further research, transport policy and EV market are discussed.

Understanding equity and travelers’ behavior plays a key role in creating suitable strategies to promote the development of the expressway. Especially, finding clusters of expressway users could help managers provide targeted policies in order to enhance service quality. However, it is challenging to identify expressway travel behaviors, such as traffic flow distribution and users’ classification. Electronic toll collection (ETC) has been widely applied to improve expressway management, because it can record the origin–destination information of users. This paper proposes a framework to analyze the equity and travel behavior of expressway users with a large amount of ETC data. In the first stage, the Gini coefficient is adopted to analyze expressway equity. In the second stage, 12 kinds of indicators are extracted, including number of trips, car type, mean distance, etc. In the third stage, kmeans algorithm is adopted to cluster the users, based on the introduced indicators. Finally, we analyze the traffic flow distribution of each group by constructing a traffic flow network. The results show that the Gini coefficient is 0.4193, which demonstrates evident inequity in the expressway service. Moreover, statistical analysis shows that expressway flow is complicated and 70.77% of travelers do not make repeat trips. It is demonstrated that expressway users can be divided into six groups, and the flow networks of cluster 2 and cluster 3 are connected more closely and evenly than other clusters are.

By using the 2017 National Household Travel Survey (NHTS) data, this study explores the status quo of ownership and usage of conventional vehicles (CVs) and alternative fuel vehicles (AFVs), i.e., Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs), in the United States. The young ages of HEVs (6.0 years), PHEVs (3.2 years) and BEVs (3.1 years) demonstrate the significance of the 2017 NHTS data. The results show that after two decades of development, AFVs only occupy about 5% of annual vehicle sales, and their share does not show big increases in recent years. Meanwhile, although HEVs still dominate the AFV market, the share of PHEVs & BEVs has risen to nearly 50% in 2017. In terms of ownership, income still seems to be a major factor influencing AFV adoption, with the median annual household incomes of CVs, HEVs, PHEVs and BEVs being $75,000, $100,000, $150,000 and $200,000, respectively. Besides, AFV households are more likely to live in urban areas, especially large metropolitan areas. Additionally, for AFVs, the proportions of old drivers are much smaller than CVs, indicating this age group might still have concerns regarding adopting AFVs. In terms of travel patterns, the mean and 85th percentile daily trip distances of PHEVs and HEVs are significantly larger than CVs, followed by BEVs. BEVs might still be able to replace CVs for meeting most travel demands after a single charge, considering most observed daily trip distances are fewer than 93.5 km for CVs. However, the observed max daily trip distances of AFVs are still much smaller than CVs, implying increasing the endurance to meet extremely long-distance travel demands is pivotal for encouraging consumers to adopt AFVs instead of CVs in the future.

With the spread of electronic toll collection (ETC) and electronic payment, it is still a challenging issue to develop a systematic approach to investigate highway travel patterns. This paper proposed to explore spatial–temporal travel patterns to support traffic management. Travel patterns were extracted from the highway transaction dataset, which provides a wealth of individual information. Additionally, this paper constructed the analysis framework, involving individual, and temporal and spatial attributes, on the basis of the RFM (Recency, Frequency, Monetary) model. In addition to the traditional factors, the weekday trip and repeated rate were introduced in the study. Subsequently, various models, involving K-means, Fuzzy C-means and SOM (Self-organizing Map) models, were employed to investigate travel patterns. According to the performance evaluation, the SOM model presented better performance and was utilized in the final analysis. The results indicated that six groups were categorized with a significant difference. Through further investigation, we found that the random traveler occupied over 40% of the samples, while the commuting traveler and long-range freight traveler presented relatively fixed spatial and temporal patterns. The results were also meaningful for highway authority management. The discussion and implication of travel patterns to be integrated with the dynamic pricing strategy were also discussed.

Electric Vehicles (EVs), by reducing the dependency on fossil fuel and minimizing the traffic-related pollutants emission, are considered as an effective component of a sustainable transportation system. However, the massive penetration of EVs brings a big challenge to the establishment of charging infrastructures. This paper presents the approach to locate charging stations utilizing the reconstructed EVs trajectory derived from the Cellular Signaling Data (CSD). Most previous work focused on the commute trips estimated from the number of jobs and households between traffic analysis zones (TAZs). This paper investigated the large-scale CSD and illustrated the method to generate the 24-hour travel demand for each EV. The complete trip in a day for EV was reconstructed through merging the time sequenced trajectory derived from simulation. This paper proposed a two-step model that grouped the charging demand location into clusters and then identified the charging station site through optimization. The proposed approach was applied to investigate the charging behavior of medium-range EVs with Cellular Signaling Data collected from the China Unicom in Tianjin. The results indicate that over 50% of the charging stations are located within the central urban area. The developed approach could contribute to the planning of future charging stations.