Communications in Transportation Research最新文献

Space–time prism (STP) is a fundamental concept in time geography and has been predominately constructed in unimodal transportation networks. Due to the vast trip chaining options by private vehicles and public transportation, it was challenging to construct STP over multimodal transportation networks. We previously put forward an efficient method to narrow down the action space for trip chaining and construct STPs effectively in a multimodal supernetwork. This study applies multimodal STP modeling to measure space–time accessibility and equality of daily activity opportunities. Two equality measures (Gini coefficient and 20:20 ratio) are derived from two space–time accessibility measures based on delineated STPs. We examine the equality of access to shopping and leisure opportunities in the Rotterdam–The Hague metropolitan area, the Netherlands. The results show the effects of various factors on accessibility and equality. It is found that although the study area has relatively low inequality using single modes, multimodal trip chaining further reduces inequality to access the distributed space–time opportunities.

Advanced integration of logistics systems has been promoted for competitiveness and sustainability. Interconnection of transport operations increases complexity at a network level, which reduces the predictability of the response of the system to disruptions. However, our understanding of the behavior of such systems is still limited. In particular, the topology of the network, which changes as the systems are integrated, is an important factor that affects the performance of the entire system. Knowledge of such mechanisms would be useful in the design and evaluation of integrated logistics. Here, we developed a simple simulation framework for logistics networks that extracts the essence of the problem. We performed extensive numerical experiments for three scenarios that mimic changes in demand: (i) locally and temporally increased traffic demand, (ii) globally and temporally increased traffic demand, and (iii) permanent change in demand pattern, under various conditions on the type of route-finding algorithm, network structure, and transportation capacity. Adaptive route-finding algorithms were more effective in square lattice and random networks, which contained many bypass routes, than in hub-and-spoke networks. Furthermore, the square lattice and random networks were robust to the change in the demand. We suggest that such preferable properties are only present in networks with redundancy and that the bypass structure is an important criterion for designing network logistics. We also performed a realistic case study that mimics interregional truck transport in Japan and confirmed that our conclusions are applicable to a practical problem.

When voyage report data is utilized as the main data source for ship fuel efficiency analysis, its information on weather and sea conditions is often regarded as unreliable. To solve this issue, this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources. These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models. Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments. For each ship, nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested. Experimental results revealed the benefits of fusing voyage report data, AIS data, and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate. Over the best datasets, the performances of several decision tree-based models are promising, including Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG). With the best datasets, their R² values over the training sets are all above 0.96 and mostly reach the level of 0.99–1.00, while their R² values over the test sets are in the range from 0.75 to 0.90. Fit errors of ET, AB, GB, and XG on daily bunker fuel consumption, measured by RMSE and MAE, are usually between 0.8 and 4.5 ton/day. These results are slightly better than our previous study, which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data, compared with the estimated geographical positions derived from the great circle route, in retrieving weather and sea conditions the ship sails through.

Ride-sourcing services have become increasingly important in meeting people's mobility needs since their emergence. Compared to traditional street-hailing taxi services, ride-sourcing services significantly reduce the matching frictions in the markets by matching drivers and passengers with relatively distant distances through an online platform. Motivated by this new feature as well as the need for designing operating and regulating strategies, researchers have attempted to describe these innovative ride-sourcing markets through mathematical models, the core of which is the matching functions for characterizing matching frictions. Previous studies have developed a variety of matching functions for ride-sourcing markets, including perfect matching function, Cobb-Douglas type matching function, queuing models, and some physical models. However, less is known about the applicability and performance of these matching functions, that is, under what situations each of these matching functions well characterizes the real market. To address this issue, this paper makes one of the first attempts to calibrate, validate, and compare the prevailing matching functions in the literature, and ascertain the conditions of their applicability. In particular, we establish a simulator to simulate a total of 420 scenarios of the ride-sourcing market under different combinations of supply and demand. The key performance metrics, including the matching rate in the market, passengers' average matching time, passengers' average pick-up time, and passengers' average total waiting time, are utilized to test and compare seven widely used matching functions under various market scenarios.

This study aims to develop a trip energy consumption (TEC) estimation model for the electric bus (EB) fleet planning, operation, and life-cycle assessment. Leveraging the vast variations of temperature in Jilin Province, China, real-world data of 31 ​EBs operating in 14 months were collected with temperatures fluctuating from −27.0 ​to 35.0 ​°C. TEC of an EB was divided into two parts, which are the energy required by the traction and battery thermal management system, and the energy required by the air conditioner (AC) system operation, respectively. The former was regressed by a logarithmic linear model with ambient temperature, curb weight, travel distance, and trip travel time as contributing factors. The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square. The latter was estimated by the operation time of the AC system in cooling mode or heating mode. Model evaluation and sensitivity analysis were conducted. The results show that: (i) the mean absolute percentage error (MAPE) of the proposed model is 12.108%; (ii) the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling; (iii) the MAPE has a 1.746% reduction if considering passengers’ boarding and alighting.

This paper analyzes the day-to-day adjustment process of users’ behaviors in a transport network which is affected by relevant alterations such as disruptions due to critical events which cause the impossibility to use one or more links. For representing the progressive adjustment of the flows on the network to reach a new equilibrium, a day-to-day discrete-time model is proposed, based on the idea that people are bounded rational in their choices, i.e. they often do not behave according to the optimal solution but they accept solutions they consider satisfying. Users, in their choice process, are influenced by the topological similarity between the route they are currently using and others. This means that they tend to prefer the solutions that are more similar to the one they are already using. In parallel, users exhibit a myopic behavior, i.e., they tend to overestimate the goodness of a route if, when using it, they suddenly experience a significant reduction in travel time compared to what they are used to. In the paper it is shown that such route choice behaviour implies that the steady state of the system corresponds to a Bounded Rational User Equilibrium, i.e., a state that does not diverge from the user equilibrium more than a certain value which increases when the relative importance given to the topological similarity grows. The model also assumes that these biases vanish, at least with respect to those routes that are most frequently used by users, after a sufficient amount of time. Under certain conditions, it is then shown that the steady state can eventually collapse into a User Equilibrium. The effectiveness of the proposed model is assessed via simulation results in which two test networks are analyzed in detail to show the evolution of the users’ behaviour in a transport network after a disruption.

Social Media have increasingly provided data about the movement of people in cities making them useful in understanding the daily life of people in different geographies. Particularly useful for travel analysis is when Social Media users allow (voluntarily or not) tracing their movement using geotagged information of their communication with these online platforms. In this paper we use geotagged tweets from 10 cities in the European Union and United States of America to extract spatiotemporal patterns, study differences and commonalities among these cities, and explore the nature of user location recurrence. The analysis here shows the distinction between residents and tourists is fundamental for the development of city-wide models. Identification of repeated rates of location (recurrence) can be used to define activity spaces. Differences and similarities across different geographies emerge from this analysis in terms of local distributions but also in terms of the worldwide reach among the cities explored here. The comparison of the temporal signature between geotagged and non-geotagged tweets also shows similar temporal distributions that capture in essence city rhythms of tweets and activity spaces.

The train platforming and routing (TPR) problem is to decide train operations within stations after the network-wise train schedules are determined. A feasible TPR plan requires both platform and route conflict-free, where the avoidance of route conflict is controlled according to three interlocking modes. Although the TPR problem is widely studied, none of them did a serious investigation on the optimality impacts of different interlocking modes in the TPR. Therefore, this paper introduced and formulated a space–time version of TPR considering three interlocking modes and subsequentially conducted numerical experiments to analyze the optimality differences under each mode. Based on the experimental findings, engineering practical suggestions are also provided. In summary, the experiment results showed that both route-locking sectional-release and sectional-locking sectional-release modes significantly outperform the route-locking route-release mode. And among them, using the route-locking sectional-release mode can bring notable benefits on large stations with high-density volumes while using the sectional-locking sectional-release mode can always provide outstanding outcomes over various station and traffic settings.

As society faces global challenges such as population growth and climate change, rethinking cities is now more imperative than ever. The design of cities can not be abstracted from the design of their mobility systems. Therefore, efficient solutions must be found to transport people and goods throughout the city efficiently and ecologically. An autonomous bicycle-sharing system would combine the most relevant benefits of vehicle-sharing, autonomy, and micro-mobility, increasing the efficiency and convenience of bicycle-sharing systems and incentivizing more people to bike and enjoy their cities in an environmentally friendly way. Due to the novelty of introducing autonomous driving technology into bicycle-sharing systems and their inherent complexity, there is a need to quantify the potential impact of autonomy on fleet performance and user experience. This paper presents the results of an agent-based simulation that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in realistic scenarios, including a rebalancing system based on demand prediction. In addition, this work describes the impact of different parameters on system efficiency and service quality. Finally, it quantifies the extent to which an autonomous system would outperform current station-based and dockless bicycle-sharing schemes. The obtained results show that with a fleet size three and a half times smaller than a station-based system and eight times smaller than a dockless system, an autonomous system can improve overall performance and user experience even with no rebalancing.

The COVID-19 pandemic has hit the transportation sector hard; particularly air transportation, as a major mode of long-distance transportation, has been affected tremendously. Since the dawn of COVID-19, politicians and policy makers have discussed the idea of introducing travel bubbles between countries (or counties), to allow for a continued exchange of people and goods. The eponymous Trans-Tasmanian travel bubble is a major example, involving quarantine-free travel between Australia and New Zealand. While both countries have tried to form a travel bubble various times, recurring setbacks and difficulties were faced. In October 2021, this ambitious project presumably has come to an end, with both countries announcing the essential capitulation of their COVIDZero strategies and a planned opening towards broader international travel. In this study, we perform a close investigation of the history behind the Trans-Tasmanian travel bubble as an on-off relationship, identifying a set of drivers for the serious problems involving a sustainable setup and operation. We develop a framework which represents important factors for successful travel bubbles and believe that the satisfaction of all factors at once is extremely challenging. Our results and insights are not specific to the Trans-Tasmanian case only, although it is taken as a running example, but can be generalized to various scales and environments. We hope that our study contributes to the literature by improving our understanding of the highly buzzed travel bubble concept, while providing empirical evidence for the troubles that inherently make such bubbles a tightrope walk.