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Transportation network companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic is limited by a lack of detailed data on the timing and location of TNC trips. This study overcomes that limitation by using data scraped from the Application Programming Interfaces of two TNCs, combined with Automated Passenger Count data on transit use and other supporting data. Using a panel data model of the change in bus ridership in San Francisco between 2010 and 2015, and confirming the result with a separate time-series model, we find that TNCs are responsible for a net ridership decline of about 10%, offsetting net gains from other factors such as service increases and population growth. We do not find a statistically significant effect on light rail ridership. Cities and transit agencies should recognize the transit-competitive nature of TNCs as they plan, regulate and operate their transportation systems.

The widespread adoption of smartphones followed by an emergence of transportation network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, have higher levels of education, and are more likely to reside in urban areas compared to the aggregate United States population. Of the TNC users, 60% hailed a ride three times or less in the previous month, indicating that TNC services are primarily used for special occasions. TNC users use public transit at higher rates and own fewer vehicles compared to the aggregate United States population. In fact, the TNC user population reported similar frequencies of use for both TNC services and public transit during the previous month. Approximately 40% of TNC users reside in regions with population densities greater than 10, 000 persons per square mile compared to only 15% for non-TNC users. Lastly, reported use of public transit for TNC users living in large cities (> 1 million) with access to heavy rail was almost three times greater when compared to similar sized cities without heavy rail. The average monthly frequency of TNC use was also elevated when heavy rail was present.

With the increasing penetration of electric vehicles (EVs), the spatial–temporal coupling between the transportation network (TN) and the power distribution network (PDN) has intensified greatly. Large-scale uncoordinated charging of EVs significantly impacts both the PDN and TN. In this paper, an optimal scheduling strategy for EV charging and discharging in a coupled power–transportation network (CPTN) with Vehicle-to-Grid (V2G) scheduling and dynamic pricing is proposed. The strategy considers the influence of dynamic transportation road network (DTRN) information on EV driving patterns, as well as the unique vehicle characteristics and mobile energy storage capabilities of EVs. Firstly, a DTRN model is established. Subsequently, the dynamic Dijkstra algorithm is utilized to accurately simulate the EV driving paths and predict the spatial–temporal distribution of the EV charging load. Secondly, optimal scheduling for EV charging and discharging within the CPTN is performed, guided by a V2G model coupled with a multi-time dynamic electricity price (MTDEP) strategy to optimize the grid load curve while accommodating the charging requirements of EVs. Finally, the effectiveness and superiority of the proposed optimization scheduling model are validated by the IEEE 33-node PDN test system.

Multimodal transportation is a modern way of cargo transportation. With the increasing demand for cargo transportation, higher requirements are being placed on multimodal transportation multi-objective routing optimization. In multimodal transportation multi-objective routing optimization, in response to the limitations of classical algorithms in solving large-scale problems with multiple nodes and modes of transport, the limitations of directed transportation networks in the application, and the uncertainty of transport time, this paper proposes an optimization framework based on multi-objective weighted sum Q -learning, combined with the proposed undirected multiple-node network, and characterizes the uncertainty of time with a positively skewed distribution. The undirected multiple-node transportation network can better simulate cargo transportation and characterize transfer information, facilitate the modification of origin and destination, and avoid suboptimal solutions due to the manual setting of wrong route directions. The network is combined with weighted sum Q -learning to solve multimodal transportation multi-objective routing optimization problems faster and better. When modeling the uncertainty of transport time, a positively skewed distribution is used. The three objectives of transport cost, carbon emission cost, and transport time were studied and compared with PSO, GA, AFO, NSGA-II, and MOPSO. The experimental results show that compared with PSO, GA, and AFO using a directed transportation network, the proposed method has a significant improvement in optimization results and running time, and the running time is shortened by 26 times. The proposed method can better solve the boundary of the Pareto front and dominate the partial solutions of NSGA-II and MOPSO. The effect of time uncertainty on the performance of the algorithm is more significant in transport orders with high time weight. With the increase in uncertainty, the reliability of the route decreases. The effectiveness of the proposed method is verified.

Transportation network companies (TNC) provide mobility services that are influencing travel behavior in unknown ways due to limited TNC trip-level data. How they interact with other modes of transportation can have direct societal impacts, prompting appropriate policy intervention. This paper outlines a method to inform such policies through a data-driven approach that specifically analyzes the interaction between TNCs and bus services in Pittsburgh, PA. Uber surge multiplier data is used over a 6-month time period to approximate TNC usage (i.e., demand over supply ratio) for ten predefined points of interest throughout the city. Bus boarding data near each point of interest is used to relate TNC usage. Data from multiple sources (weather, traffic speed data, bus levels of service) are used to control for conditions that influence bus ridership. We find significant changes in bus boardings during periods of unusually high TNC usage at four locations during the evening hours. The remaining six locations observe no significant change in bus boardings. We find that the presence of a dedicated bus way transit station or a nearby university (or dense commercial zones in general) both influence ad-hoc substitutional behavior between TNCs and public transit. We also find that this behavior varies by location and time of day. This finding is significant and important for targeted policies that improve transportation network efficiency.

Coordination between the construction of transport infrastructure and the development and protection of territorial space is an important factor in promoting sustainable regional development, but there is still a lack of systematic research on the impact of transport on territorial space worldwide. Following the logic of “development trend revealing—theoretical and technological summary—mechanism analysis”, the research progress on the two aspects of development and protection of territorial space related to transport is systematically and comprehensively sorted from the perspective of multi-scale and multi-mode transport. The results show that: (1) The number of research papers on the effect of transport on territorial space is on the rise, and there is an obvious trend of cross-disciplinary research. (2) Transport infrastructure will promote the development of territorial space in terms of land use change, spatial-temporal compression, and economic development, and will affect the protection of territorial space in terms of ecological impacts, energy consumption and carbon emissions, and the crossing of protection zones. (3) In the existing research, the lack of multi-dimensional indicator system construction and analysis, insufficient research at the mechanism level, and insufficient combination of theoretical research and practical application are the main problems at present, and an important direction is urgently needed for future research.

Despite growing scholarly attention on the role of urban networks for understanding regional dynamics, there has been limited research examining the impact of cities’ transportation network connections on regional market integration. This paper analyzes China’s four major urban agglomerations: the Yangtze River Delta, the Pearl River Delta, Beijing-Tianjin-Hebei, and Chengdu-Chongqing. Applying a spatial Durbin model to cross-sectional datasets for 2019, we provide insight into the role of cities’ transportation network connections in promoting regional market integration, considering both the potentially heterogeneous impact of network connections and the interplay between network and agglomeration externalities. Our results indicate that: (1) cities’ transportation network connections have an inverted ‘U’-shaped effect on regional market integration; (2) transportation network connections have spatial spillover effects; (3) the positive impact of transportation network connections on regional market integration becomes more pronounced as city size decreases; and (4) there are neither complementary nor substitution effects between network and agglomeration externalities. We reflect on the broader implications of our empirical findings for regional development strategies and discuss possible avenues for further research.

In last decades, air transportation plays an important role in global economy. Several scholars have studied optimizing air transportation system or proposed reliability evaluation algorithms from airline management viewpoints. This work evaluates the reliability of an air transportation system from the perspective of travel agency instead. An air transportation system can be modeled as a multi-state air transportation network (MATN) wherein each node represents an airport and each arc denotes a flight carrying passengers between a pair of airports from scheduled departure time to scheduled arrival time. Significantly, this study focuses on investigating the reliability of multiple travel demands. Therefore, the reliability of an MATN is defined as the probability that a set of demands can be carried successfully under constraints of time and number of stopovers. This study employs the concept of minimal paths in reliability evaluation. Subsequently, a searching procedure is added to the proposed algorithm. In addition, an illustrative example and a case study are utilized to demonstrate the proposed algorithm and discuss the implications of reliability evaluation for the management of travel agency.

Cloud manufacturing is an emerging service-oriented manufacturing paradigm that integrates and manages distributed manufacturing resources through which complex manufacturing demands with a high degree of customization can be fulfilled. The process of service selection optimization and scheduling (SSOS) is an important issue for practical implementation of cloud manufacturing. In this paper, we propose new mixed-integer programming (MIP) models for solving the SSOS problem with basic composition structures (i.e., sequential, parallel, loop, and selective). Through incorporation of the proposed MIP models, the SSOS with a mixed composition structure can be tackled. As transportation is indispensable in cloud manufacturing environment, the models also optimize routing decisions within a given hybrid hub-and-spoke transportation network in which the central decision is to optimally determine whether a shipment between a pair of distributed manufacturing resources is routed directly or using hub facilities. Unlike the majority of previous research undertaken in cloud manufacturing, it is assumed that manufacturing resources are not continuously available for processing but the start time and end time of their occupancy interval are known in advance. The performance of the proposed models is evaluated through solving different scenarios in the SSOS. Moreover, in order to examine the robustness of the results, a series of sensitivity analysis are conducted on key parameters. The outcomes of this study demonstrate that the consideration of transportation and availability not only can change the results of the SSOS significantly, but also is necessary for obtaining more realistic solutions. The results also show that routing within a hybrid hub-and-spoke transportation network, compared with a pure hub-and-spoke network or a pure direct network, leads to more flexibility and has advantage of cost and time saving. The level of saving depends on the value of discount factor for decreasing transportation cost between hub facilities.

Transportation Network Company (TNC) services have become a prominent factor in urban transportation in recent years, and there is an ongoing debate regarding their relationship with public transit. While many argue that TNCs draw passengers away from public transportation, others believe the two modes complement each other. However, due to the inadequate sample size of rider surveys as primary data sources, our understanding of how riders choose between these two modalities remains limited. This study uses nine months of trip planning data generated by the Transit App, which captures how travelers engage with multiple options in real time, including TNC and public transit services. We extract measures from Transit describing the travel options and the habits of each individual user for sessions in which the user “tapped” on one of these two modes, indicating consideration of it as an option. Machine learning models predict the likelihood of a rider tapping TNC based on features of the available public transit options and other contextual factors (e.g., time of day, weather conditions). The models find that these taps are driven by factors that highlight the convenience of TNC, such as the waiting time, walking distance, and the number of transfers for public transportation trips. We also find that the majority of TNC trips tapped by app users combine the two modes when using the Transit App, with TNC acting as a connection to or from public transit. These results provide detailed additional evidence for current arguments for both competition and complementarity between TNC and public transit from a population that uses an app to navigate public transit.