Search Results (37)

Accurate travel time prediction is essential for improving urban mobility, traffic management, and ride-hailing services. Traditional link- and path-based models face limitations due to data sparsity, segmentation errors, and computational inefficiencies. This study introduces an origin–destination (OD)-based travel time prediction framework leveraging high-resolution ride-hailing trajectory data. Unlike previous works, our approach systematically integrates spatiotemporal, quantified weather metrics and driver behavior clustering to enhance predictive accuracy. The proposed model employs a Back Propagation Neural Network (BPNN), which dynamically adjusts hyperparameters to improve generalization and mitigate overfitting. Empirical validation using ride-hailing data from Xi’an, China, demonstrates superior predictive performance, particularly for medium-range trips, achieving an RMSE of 202.89 s and a MAPE of 16.52%. Comprehensive ablation studies highlight the incremental benefits of incorporating spatiotemporal, weather, and behavioral features, showcasing their contributions to reducing prediction errors. While the model excels in moderate-speed scenarios, it exhibits limitations in short trips and low-speed cases due to data imbalance. Future research will enhance model robustness through data augmentation, real-time traffic integration, and scenario-specific adaptations. This study provides a scalable and adaptable travel time prediction framework, offering valuable insights for urban traffic management, dynamic route optimization, and sustainable mobility solutions within ITS. Full article

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations. Full article

High precision in forecasting travel demand for online car-hailing is crucial for traffic management to schedule vehicles, hence reducing energy consumption and achieving sustainable development. Netflix demand forecasting relies on the capture of spatiotemporal correlations. To extract the spatiotemporal information more fully, this study designs and develops a novel spatiotemporal prediction model with multidimensional inputs (MSACL) by embedding a self-attention memory (SAM) module into a convolutional long short-term memory neural network (ConvLSTM). The SAM module can extract features with long-range spatiotemporal dependencies. The experimental data are derived from the Chengdu City online car-hailing trajectory data set and the external factors data set. Comparative experiments demonstrate that the proposed model has higher accuracy. The proposed model outperforms the Sa-ConvLSTM model and has the highest prediction accuracy, shows a reduction in the mean absolute error (MAE) by 1.72, a reduction in the mean squared error (MSE) by 0.43, and an increase in the R-squared (R²) by 4%. In addition, ablation experiments illustrate the effectiveness of each component, where the external factor inputs have the least impact on the model accuracy, but the removal of the SAM module results in the most significant decrease in model accuracy. Full article

Online car-hailing is gradually shifting towards a predominant use of electric vehicles, a change that is advantageous for developing a sustainable society. Understanding the patterns of changes in online car-hailing travel can assist transportation authorities in optimizing vehicle dispatching, reducing idle rates, and minimizing resource wastage. The built environment influences the demand for online car-hailing travel. Previous studies have commonly employed the geographically weighted regression (GWR) model and the geographically and temporally weighted regression (GTWR) model to examine the relationship between the demand for online car-hailing trips and the built environment. However, these studies have ignored that the impact range of the built environment also varies with time and space. To fully consider the variations in the impact range of the built environment, this study established multi-scale geographically and temporally weighted regression (MGTWR) to examine the spatiotemporal impacts of urban built environments on the demand for online car-hailing travel. An empirical study was conducted to assess the effectiveness of the MGTWR model using point of interest (POI) data and online car-hailing order data from Haikou. The evaluation indicators showed that the MGTWR model has higher fitting accuracy than the GTWR model. Moreover, the impact of each type of POI on the demand for online car-hailing travel was analyzed by examining the temporal and spatial distribution of the regression coefficients. Additionally, we observed that transport facility POIs and healthcare service POIs exerted the most pronounced influence on the demand for online car-hailing. In contrast, the impact of shopping service POIs and catering service POIs was relatively weaker. Full article

The COVID-19 pandemic and lockdown have caused serious impacts on people’s lives, especially on daily travel like online car-hailing. Understanding the impacts of the pandemic on online car-hailing travel is essential for sustainable urban planning and governance, especially during public health emergencies including COVID-19. However, few studies have delved into the in-depth patterns and interpretations of crowd behaviors and mobility variations resulting from the lockdown, especially from different perspectives. This study attempts to make contributions to this gap by building a three-step method from a macroscopic to mesoscopic perspective. A dataset of online car-hailing trajectories for 15 days in 2018 and 3 special days (before and after the lockdown) in 2022 was used. Detailed analyses of the overall spatiotemporal patterns, the flows between administrative districts, and the four-perspective investigation in the central urban area were conducted. The main findings include a dramatic plunge in ride counts for online car-hailing due to the lockdown and a significant change in human mobility associated with hospitals and traffic hubs. Our study provides insights into the understanding of impacts of COVID-19 and lockdown and hopefully helps with the resilience and sustainability of the city. The workflow might also be inspiring for further studies. Full article

With the advent of the information age and rapid population growth, the urban transportation environment is deteriorating. Travel-route planning is a key issue in modern sustainable transportation systems. When conducting route planning, identifying the spatiotemporal disparities between planned routes and the routes chosen by actual drivers, as well as their underlying reasons, is an important method for optimizing route planning. In this study, we explore the spatial–temporal differences between planned routes and actual routes by studying the popular roads which are avoided by drivers (denoted as: PRAD) from car-hailing trajectories. By applying an improved Hidden Markov Model (HMM) map-matching algorithm to the original trajectories, we obtain the Origin-Destination (OD) matrix of vehicle travel and its corresponding actual routes, as well as the planned routes generated by the A* routing algorithm. We utilize the Jaccard index to quantify the similarity between actual and planned routes for the same OD pairs. The causes of PRADs are detected and further analyzed from the perspective of traffic conditions. By analyzing ride-hailing trajectories provided by DiDi, we examine the route behavior of drivers in Wuhan city on weekdays and weekends and discuss the relationship between traffic conditions and PRADs. The results indicate that the average accuracy of GNSS trajectory point-to-road map-matching reaches 88.83%, which is approximately 12% higher than the accuracy achieved by the HMM map-matching method proposed by Hu et al. Furthermore, the analysis of PRAD causes reveals that PRADs occurring on weekdays account for approximately 65% and are significantly associated with traffic congestion and accidents during that time. The findings of this study provide insights for future research on sustainable transportation systems and contribute to the development of improved route-planning strategies. Full article

Leisure trips have become more important in an era where people are increasingly concerned with quality of life. Leisure trips are unique in that they are not as strict as mandatory trips, while, at the same time, they have wider characteristics because of their flexibility. Research on leisure trips from developing countries is still under-represented as there is still a focus on commuting trips. This study aims to identify factors that influence the mode of transportation choice for leisure trips by domestic travelers who live in cities surrounding Bandung, Indonesia. Data were collected using stated-preference self-report questionnaires distributed to locals who have the intention to travel for leisure in Bandung in the future. Based on responses from 305 respondents with a total number of 1220 observations, a multinomial logit model was estimated. It was found that trains and buses were selected more often by locals than other modes of transportation, including private cars, for leisure trips. Our model showed that locals considered travel time and travel costs as the most significant factors in selecting the mode of transportation for their leisure trips. Besides the existence of online transportation—hailing rides through mobile apps—as an alternative, this study also reveals payment method to be a unique consideration of locals when travelling leisurely in this digital era. Full article

With the development of cities, the organization of jobs–housing space is becoming more complex, and the rapid, effective identification of both residences and workplaces is crucial to sustainable urban development. The long time series of online car-hailing data conveys a large amount of activity trajectory information about urban populations, which can represent the social functions of urban areas, including workplaces and residences. This paper constructs a jobs–housing site identification model based on human activity characteristics. This model uses a time series dataset of online car hailing that characterizes the changes in regional passenger flow and implements the similarity measure and semi-supervised learning of time series to determine the classification of urban areas. Then, the jobs–housing factor method is introduced to extract the jobs–housing characteristics of different regions, which achieves the jobs–housing site identification. Finally, the empirical analysis of Chengdu city shows that the proposed model method can effectively mine the distribution of urban jobs–housing sites. The identification results are consistent with the actual situation, and the combination of the time series similarity and the jobs–housing feature variable improves the identification effect, providing a new way of thinking about urban jobs–housing space research. Full article

Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and divides each day into 48 time units (30 min per unit) to form a data set. Taking the minimum average absolute error as the optimization objective, the historical data sets are classified, and the values of the state vector T and the parameter K of the K-nearest neighbor model are optimized, which solves the problem of prediction error caused by fixed values of T or K in traditional model. The conclusion shows that the forecasting accuracy of the K-nearest neighbor model can reach 93.62%, which is much higher than the exponential smoothing model (81.65%), KNN1 model (84.02%) and is similar to LSTM model (91.04%), meaning that it can adapt to the urban online car-hailing system and be valuable in terms of its potential application. Full article

A sharing economy based on improved ICT is an emerging economic−technological concept. Sharing economy-enabled digital platforms in China have changed patterns of consumption, exploited under-utilized resources, and increased employment. Previous studies on sharing economy-enabled digital platforms mainly focused on the positive and negative effects, users’ perception and behavioral intention, and the business model, but few studies have addressed these platforms for socioeconomic development from the perspective of legitimacy. This study applied legitimacy to analyze a typical sharing economy-enabled digital platform in China for socioeconomic development via a longitudinal interpretive case study. A process model of variation and evolution of an online car-hailing platform for socioeconomic development was inductively derived, allowing elucidation of the complexities and interplay of regulative challenges, normative challenges, and cognitive challenges in each developmental phase, resulting in improving and enriching the way people go out, optimizing resource allocation, increasing employment, and undertaking social responsibility. The findings of this case study provide a comprehensive and supported framework and demonstrate a successful model for managers and other peer organizations for future business efforts in the sharing economy. Full article

With the development of the “Internet +” model and the sharing economy model, the “online car-hailing” operation model has promoted the emergence of “online-hailing agricultural machinery”. This new supply and demand model of agricultural machinery has brought greater convenience to the marketization of agricultural machinery services. However, although this approach has solved the use of some agricultural machinery resources, it has not yet formed a scientific and systematic scheduling model. Referring to the existing agricultural machinery scheduling modes and the actual demand of agricultural production, based on the idea of resource sharing, in this research, the soft and hard time windows were combined to carry out the research on the dynamic demand scheduling strategy of agricultural machinery. The main conclusions obtained include: (1) Based on the ideas of order resource sharing and agricultural machinery resource sharing, a general model of agricultural machinery scheduling that meet the dynamic needs was established, and a more scientific scheduling plan was proposed; (2) Based on the multi-population coevolutionary genetic algorithm, the dynamic scheduling scheme for shared agricultural machinery for on-demand farming services was obtained, which can reasonably insert the dynamic orders on the basis of the initial scheduling scheme, and realize the timely response to farmers’ operation demands; (3) By comparing with the actual production situation, the path cost and total operating cost were saved, thus the feasibility and effectiveness of the scheduling model were clarified. Full article

Online car-hailing has brought convenience to daily travel, whose accurate prediction benefits drivers and helps managers to grasp the characteristics of urban travel, so as to facilitate decisions. Spatiotemporal prediction in the transportation field has usually been based on a recurrent neural network (RNN), which has problems such as lengthy computation and backpropagation. This paper describes a model based on a Transformer, which has shown success in computer vision. The study area is divided into grids, and the structure of travel data is converted into video frames by time period, based on predicted spatiotemporal travel demand. The predictions of the model are closest to the real data in terms of spatial distribution and travel demand when the data are divided into 10 min intervals, and the travel demand in the first two hours is used to predict demand in the next hour. We experimentally compare the proposed model with the three most commonly used spatiotemporal prediction models, and the results show that our model has the best accuracy and training speed. Full article

The consistent innovations and applications of information technology drive the vigorous development of the gig economy, and generate gig workers such as food delivery workers and couriers, and make a great contribution to stabilizing employment and increasing income. Gig workers, mostly made up of migrants, and suffer from job and status difficulties. Research on the well-being of migrant gig workers can reveal the practical problems and provide suggestions for narrowing the wealth gap to promote social fairness and justice. Taking Hangzhou city in China as an example, this paper explores the well-being of food delivery workers, couriers, and online car-hailing drivers as representatives of migrant gig workers. Firstly, the relevant data are acquired through the questionnaire. Secondly, the characteristics of this group are analyzed through descriptive analysis, namely: most of them are migrant workers aged between 20 and 39 with low occupation satisfaction due to insufficient social security coverage and limited well-being, despite relatively high income. Based on the analysis of differences in demographic variables and structural equation modeling, the factors affecting the well-being of migrant gig workers are studied, which mainly are occupation satisfaction, social interaction, and social security. The results show that occupation satisfaction is positively affected by family characteristics, social interaction, and social security. In addition, family characteristics and social security positively impact social interaction, but the former has no significant effect on well-being. Finally, this paper enriches the research on the well-being of specific migrant gig workers and gives policy suggestions for enhancing the well-being of migrant gig workers in Hangzhou city from the perspective of optimizing the mechanism, pilot construction, and platform provision. Full article

Urban passenger transport is one of the most significant sources of fossil energy consumption and greenhouse gas emission, especially in developing countries. The rapid growth of urban transport makes it a critical target for carbon reduction. This paper establishes a method for calculating carbon emission from urban passenger transport including ground buses, private cars, cruising taxis, online-hailing taxis, and rail transit. The scope of the study is determined according to the transportation mode and energy type, and the carbon emission factor of each energy source is also determined according to the local energy structure, etc. Taking into consideration the development trend of new energy vehicles, a combination of “top-down” and “bottom-up” approaches is used to estimate the carbon dioxide emission of each transportation mode. The results reveal that carbon emission from Qingdao’s passenger transport in 2020 was 8.15 million tons, of which 84.31% came from private cars, while the share of private cars of total travel was only 45.66%. Ground buses are the most efficient mode of transport. Fossil fuels emit more greenhouse gases than other clean energy sources. The emission intensity of hydrogen fuel cell buses is better than that of other fuel type vehicles. Battery electric buses have the largest sensitivity coefficient, therefore the carbon emission reduction potentially achieved by developing battery electric buses is most significant. Full article

The study of urban functional zoning is not only important for analyzing urban spatial structure but also for optimizing urban management and promoting scientific urban planning. Different areas undertaking different urban functions correspond to different traffic patterns and specific cycles. Here, a method named Urban Functional Zoning based on the Spatial Specificity (UFZ-SS) is proposed. The core of this method is to obtain urban spatial zoning through the specific cycles of traffic flows. First, UFZ-SS uses the Ensemble Empirical Modal Decomposition (EEMD) method to extract the specific periodic signal characteristics of traffic flows. Second, UFZ-SS calculates the contribution of online car-hailing traffic of different cycles in each zone. Then, the Gaussian Mixture Model (GMM) is utilized to classify all spatial zones into different spatial partitions based on the contribution of each periodic signal. Finally, this study validates UFZ-SS with the online car-hailing traffic volume in northeast Chengdu, China. The results show that the periodic characteristics of traffic can be effectively extracted and analyzed by the EEMD method, and highly distinct and accurate urban spatial partitioning results can be derived by spatial clustering based on the measures of specific cycles. Moreover, with the assistance of Point of Interest (POI) data, we verify the functional zones and structural patterns, which further demonstrates the validity and rationality of urban functional zones identified by UFZ-SS. This study provides a new potential perspective for the identification of urban functional zones, which may lead to a better understanding of the urban spatial structure and even urban planning. Full article