Life Cycle Assessment of Free-Floating Bike Sharing on Greenhouse Gas Emissions: A Case Study in Nanjing, China

The free-floating bike sharing (FFBS) system appears in the form of low-carbon transport mode. Life cycle assessment (LCA) is a method to analyze the environmental impact of FFBS but has rarely considered the trip chain if the intermodal transport modes were employed. This paper proposes a mathematical formalization of LCA in response to the trip chain. The environmental benefit of FFBS was analyzed by this method considering the production, use, operation, and disposal phases in Nanjing. An online survey was conducted to analyze the mechanism of modal shift influenced by FFBS. The results showed that most respondents only use FFBS in the trip, with savings of 63.726 g CO2-eq/p·km, mainly shifting from lower-emission modes (28.30% from bus, 14.86% from metro, and 33.97% from non-motorized modes), while the trip mode of connecting public transport with FFBS could better replace the motorized transport trip and generate better low-carbon benefits with savings of 300.718 g CO2-eq/p·km. One FFBS should be used for at least 227 days to generate positive environmental benefits based on the current number of FFBS and the assumption of the utilization of each bike, which is once a day on average. The research results can effectively support the environmental benefit analysis of FFBS, the subsequent planning based on the low-carbon concept, and the implementation of relevant incentive policies.

Forecasting Delivery Pattern through Floating Car Data: Empirical Evidence

This paper investigates the opportunities offered by floating car data (FCD) to infer delivering activities. A discrete trip-chain order model (within the random utility theory) for light goods vehicles (laden weight less than 3.5 tons) is hence proposed, which characterizes delivery tours in terms of the number of stops/deliveries performed. Thus, the main goal of the study is to calibrate a discrete choice model to estimate the number of stops/deliveries per tour by using FCD, which can be incorporated in a planning procedure for obtaining a preliminary assessment of parking demand. The data used refer to light goods vehicles operating in the Veneto region. The database contains more than 8000 tours undertaken in 60 working days. Satisfactory results have been obtained in terms of tour estimation and model transferability.

Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data

Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on evaluating the effectiveness of these models on large-scale location data. This paper proposes to use travel location data from an Internet company and travel data from transport department to identify travel modes. A multiple binary classification model based on data fusion is used to find out the relationship between travel mode and different features. Firstly, we enlisted volunteers to collect travel data and record their travel trip process using a custom-developed WeChat program. Secondly, we have developed three binary classification models to explain how different attributes can be used to model travel mode. Compared with one multi-classification model, the accuracy of our model improved significantly, with prediction accuracies of 0.839, 0.899, 0.742, 0.799, and 0.799 for walk, metro, bike, bus, and car, respectively. This suggests that the model could be applied not only in engineering practice to identify the trip chain from Internet location data but also in decision support for transportation planners.

What Counts as Commute Travel? Identification and Resolution of Key Issues around Measuring Complex Commutes in the National Household Travel Survey

We use travel diary data from the 2017 National Household Travel Survey (NHTS) Georgia subsample to address critical issues associated with analyzing complex work journeys. To define the work journey, we discuss the importance of defining commute anchors by both purpose and location. We then compare two alternate measures for determining what portion of each journey should be counted as commute distance: the last leg of the journey (the NHTS default), and a modeled counterfactual simple commute to estimate the distance that would have been traveled had no stops been made. The average complex commute distance obtained using the counterfactual method was 63% higher than the estimate based on using the last leg alone. Using the last-leg method may understate Georgia’s annual commute distance by 2.6 billion miles (10% of the total, including both simple and complex commutes). We argue that the last-leg method is not an accurate gauge of work travel, particularly among populations such as women, who are more likely to trip chain on their commutes.

Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory

Suburban tourist railway is an emerging transportation mode for tourism. Knowing the travel demand and trip distribution patterns of tourists is an important prerequisite to the planning and construction of suburban tourist railways. However, this issue has attracted very little research attention so far. Therefore, this paper proposes a forecasting model focused on the trip distribution of tourists who travel with the suburban tourist railway. Based on the analysis of the characteristics of tourists’ trips and the use of the trip chain method, the frequency, order, distance, and visiting volume of stay points of the trips of tourists have been intensively studied. Then, a tourist trip distribution forecasting model was built in this paper. It uses the Entropy-Maximizing theory to predict trip chain distribution probability and obtain the distribution of tourists within the city. A case study that takes the H city as an example was conducted to test the proposed model. The results of this case show that the output of the model can reflect the real trip distribution characteristics of tourists very well, which demonstrates the applicability and effectiveness of the proposed model.

Charging Strategy for Electric Vehicles Considering Consumer Psychology and Trip Chain

As a kind of movable storage device, the electrical vehicles (EVs) are able to support load shaving through orderly charging. The existing researches mostly focus on the design of EVs charging control technology with little consideration of trip-chain-based consumer psychology of EV owners. To fill this gap, this article proposes a price-based orderly charging strategy for EVs considering both consumer psychology and trip chain. Then, the load shaving problem is transformed into a multiobjective optimization problem, to minimize peak-to-valley difference and network loss. A time-of-use price optimization model based on consumer psychology is established to describe the charging behavior of EV owners influenced by electricity price. Finally, the examples verify the feasibility of the proposed strategy by comparing the impact of EVs connected to grid under different ratios, different load transfer rates, and different scenarios.