Taxi Travel Research Articles

A novel tucker decomposition driven taxi travel demand forecasting algorithm

<p>This research aims to enhance taxi travel demand forecasting for sustainable urban traffic management and planning. We extend the Seasonal Autoregressive Integrated Moving Average model into a high-dimensional tensor form by treating the urban transport network as a Euclidean space and introducing Tucker decomposition. This novel approach, both theoretically significant and practically applicable, represents time series data as tensors to better capture multimodal structures and correlations, improving predictive accuracy. Tucker decomposition reduces computational complexity and memory requirements, making it ideal for large-scale urban traffic network prediction. Experiments on six real-world datasets show that the model’s MAE and RMSE are reduced by about 39.43% and 27.01% on average, respectively, compared to the baseline model. Notably, the model is computationally very efficient and takes only a relatively short time to train., suitable for real-time traffic management, congestion mitigation, and resource optimization. In summary, this work innovates time series analysis, providing an efficient and precise tool for urban traffic management and planning, contributing to sustainable urban transportation advancement.<strong></strong></p>

Presenting the behavioral model of citizens in selection of trip vehicle with emphasis on how to go to work

Understanding the factors that influence individual vehicle choice behavior will reveal changes in preferences and attitudes. These changes can provide insights into travel patterns, improve transportation planning, prepare for future infrastructure needs and services, and inform better design and implementation of sustainable and inclusive transportation. The purpose of this research is to provide a behavioral model of citizens in choosing a travel vehicle with an emphasis on how to go to work in Shiraz, Iran. The statistical method used in this research is the logit mathematical model. SPSS software was used to analyze the research results. Different hypothetical scenarios were investigated according to the final vehicle selection model and the possibility of choosing different travel vehicles in each of the hypothetical scenarios. The results of the comparison of logit models for all travel destinations showed that the behavior of Shiraz citizens in choosing a means of transportation is not nested. For this reason, the final model was chosen for business trips. The results obtained from the hypothetical scenarios of the business travel selection tool in Shiraz showed that if none of the demand management policies and the creation of high-speed buses are applied, the share of private cars is 38%, the share of buses and subways is 32%, the share of taxi travel is 30%, and the share of walking will be 33 %. Also, social conditions and demographic characteristics do not have a significant effect on the choice of vehicle and travel behavior of citizens to the workplace.

Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.

The Optimization of Urban Traffic Routes Using an Enhanced Genetic Algorithm: A Case Study of Beijing South Railway Station

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system.

The Association between Rainfall and Taxi Travel Activities: A Case Study from Wuhan, China

Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall’s impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people’s mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents’ travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions.

Mathematical Modeling on Integrated Vehicle Assignment and Rebalancing in Ride-hailing System with Uncertainty Using Fuzzy Linear Programming

The general public frequently uses taxis as local transportation to get from one location to another. Ride-hailing is an innovation in taxi services that lets customers use their smartphones to find drivers, find prices, and submit requests. The two parts of ride-hailing are vehicle assignment and rebalancing. The task of the assignment is to allocate resources as efficiently as possible to fulfil demand. Demand and supply in the area are brought into balance through the rebalancing process. The rebalancing step is frequently carried out independently of the assignment process in ride-hailing pickup systems. However, it can be integrated into a single optimization process to improve system performance. The linear programming approach can integrate assignment and rebalancing. The batch assignment is an assignment algorithm in which each supply and demand is collected within a specific time window. The assignment is done after the vehicles and requests are collected. Fuzzy linear programming is used to deal with environmental uncertainty. Uncertain demand can reduce the reliability of ride-hailing pickup systems in addressing the problem of allocating empty vehicles to areas of high demand. The pickup time may change over time due to traffic conditions. Assignment integration and rebalancing modelling with fuzzy parameters are carried out to obtain a ride-hailing pickup system model that can handle demand uncertainty, pickup travel time, and travel delay times and increase the ride-hailing effectiveness of the pickup system. Numerical simulations were carried out on the assignment and rebalancing integration model without and with uncertainty parameters—numerical simulations based on publicly available taxi travel request data. The model selection for the ride-hailing system can be determined based on the numerical simulation results. This research can potentially advance and expand knowledge, especially in transportation.

Electric Taxi Charging Load Prediction Based on Trajectory Data and Reinforcement Learning—A Case Study of Shenzhen Municipality

In order to effectively solve the problem of electric taxi charging load prediction and reasonable charging behaviour discrimination, in this paper, we use taxi GPS trajectory data to mine the probability of operation behaviour in each area of the city, simulate the operation behaviour of a day by combining it with reinforcement learning ideas, obtain the optimal operation strategy through training, and count the spatial and temporal distributions and power values at the time of charging decision making, so as to predict the charging load of electric taxis. Experiments are carried out using taxi travel data in Shenzhen city centre. The results show that, in terms of taxi operation behaviour, the operation behaviour optimized by the DQN algorithm shows the optimal effect in terms of the passenger carrying time, mileage, and daily net income; in terms of the charging load distribution, the spatial charging demand of electric taxis in each area shows obvious differences, and the charging demand load located in the city centre area and close to the traffic hub is higher. In time, the peak charging demand is distributed around 3:00 to 4:00 and 14:00 to 15:00. Compared with the operating habits of drivers based on the Monte Carlo simulation, the DQN algorithm is able to optimise the efficiency and profitability of taxi drivers, which is more in line with the actual operating habits of drivers formed through accumulated experience, thus achieving a more accurate charging load distribution.

Exploring multi-relational spatial interaction imputation with distance-decay effects

ABSTRACT Spatial interaction imputation aims to compensate for missing stable connections in geographical space, bolstering interaction network integrity and accuracy. Graph neural networks excel in graph-structured interaction data. However, existing research often focuses on homogeneous networks, neglecting the impact of heterogeneous interaction relationships influenced by distance decay on interaction imputation. Neglecting edge heterogeneity constrains the ability to effectively model the network structure, consequently leading to suboptimal performance in interaction imputation. This study introduces an interaction imputation graph convolutional network model. It constructs a heterogeneous interaction network with multi-distance relationships, considering distance decay. The model performs graph embedding based on interaction relationships between nodes. It comprehensively incorporates multiple interaction modes, topological structures, and node attributes to enhance spatial interaction imputation accuracy. Empirically validated using Beijing taxi travel data, our model outperforms existing models, improving imputation accuracy by approximately 8.70%. Our model consistently maintains superior accuracy in interaction networks of various sizes, demonstrating the stable superiority of our model. We also demonstrated that the variation in the number of interaction relationships affects imputation accuracy. A reasonable number of relationships and a larger feature dimension of geographical units yield better interaction imputation results.

Simulation Optimization of Taxi Travel System at Terminal T2 of Shanghai Hongqiao Airport: Enhancing Customer Satisfaction

Simulation Optimization of Taxi Travel System at Terminal T2 of Shanghai Hongqiao Airport: Enhancing Customer Satisfaction

Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning

There is a growing interest in the optimization of vehicle fleets management in urban environments. However, limited attention has been paid to the integrated optimization of electric taxi fleets accounting for different operations as well as complex spatiotemporal demand dynamics. To this end, this study develops a real-time recommendation framework based on deep reinforcement learning (DRL) for electric taxis (E-taxis) to improve their system performance with explicit modeling of multiple vehicle actions and varying travel demand across space and over time. Spatiotemporal patterns of urban taxi travels are extracted from large-scale taxi trajectories. Spatiotemporal strategies are proposed to coordinate E-taxis’ repositioning and recharging with optimized recommendation for next destinations and charging stations. A spatiotemporal double deep Q-network (ST-DDQN) is embedded in the DRL framework to maximize the daily profit. A prototype real-time recommendation system for E-taxis is implemented for the decision-making of E-taxi drivers and sensitivity analyses are carried out. The experimental results in Shenzhen, China suggest that the proposed framework could improve the overall performance. This study will benefit the promotion of connected E-taxis and the development of clean and smart transportation.

Influence of the built environment on taxi travel demand based on the optimal spatial analysis unit.

When discussing the influence of the built environment on taxi travel demand, few studies have considered the effect of the modifiable areal unit problem (MAUP) or the influence of the "5D" dimensions of the built environment (It refers to the consideration of the built environment from five dimensions of density, diversity, design, destination accessibility and distance to transit.) on taxi travel demand. Moreover, discussion of the nonlinear and linear relationships between taxi demand and environment variables is also lacking. To address these gaps, we constructed a "5D" dimension index system of built environment variables. The influence of the MAUP on the model results was discussed using the optimal parameter-based geographical detector (OPGD) model, and the optimal spatial analysis unit was selected. The OPGD and multiscale geographically weighted regression (MGWR) models were used to reveal the influence of different dimensions of the built environment on taxi travel demand from global and local perspectives, respectively. Finally, the central urban area of Xi'an was analyzed as an example. The results show the following: (1) Most built environment variables are sensitive to the influence of MAUP. (2) It is better to divide the space into regular hexagons than squares, and the optimal spatial analysis unit in this study is a regular hexagon grid with sides of 900m. (3) From a global perspective, the distance to the city center, commercial residence POI density, transportation facility POI density, and population density have the greatest influence on the demand for taxi travel. (4) From a local perspective, the MGWR model considering spatial heterogeneity and scale differences is superior to the GWR model, and the influence of built environment variables exhibited spatial heterogeneity. The proposed optimal spatial analysis unit can provide a basis for taxi demand forecasting and scheduling. This study provides a reference for urban planners and traffic managers to offer optimization strategies related to the built environment, promote healthy development of the taxi industry, and solve the problems of the urban transportation system.

Will the Order Be Canceled? Order Cancellation Probability Prediction Based on Deep Residual Model

Online ride-hailing is gradually becoming one of the indispensable travel modes for people. Online ride-hailing platforms receive tens of thousands of taxi travel orders every day. However, some orders are canceled by the user after the driver answers. User cancellation of orders will disrupt the regular operation of the taxi platform, reduce the efficiency and income of drivers on the online ride-hailing platform, and affect the ride experience of other users. To capture the relationship between the cancellation probability of online taxi orders and users, we first analyze the factors that affect users’ cancellation of online taxi orders. We find that the cancellation probability of online ride-hailing orders is highly correlated with the travel time of the online taxi, the distance between vehicle and passengers, road congestion conditions, and alternative transportation resources around the passengers. Second, a deep residual network-based ride-hailing order cancellation probability prediction model (DeepOCP) is designed to predict the probability of a user canceling an answered online-hailing order. Finally, the Didi Chengdu Express public data set is used to test the model’s effectiveness. This paper is the first study to use a deep-learning model to predict the cancellation probability of online ride-hailing orders.

Latent Semantic Sequence Coding Applied to Taxi Travel Time Estimation

Taxi travel time estimation based on real-time traffic flow collection in IoT has been well explored; however, it becomes a challenge to use the limited taxi data to estimate the travel time. Most of the existing methods in this scenario rely on shallow feature engineering. Nevertheless, they have limited performance in learning complex moving patterns. Thus, a Latent Semantic Pulse Sequence-based Deep Neural Network (LSPS-DNN) is proposed in this paper to improve the taxi travel time estimation performance by constructing a latent semantic propagation graph representing the latent path sequence. It first extracts the shallow modal features of trips, such as the time period and spatial location at different granularities. The representation of the pulse propagation graph is then extracted from shallow spatial features using a Pulse Coupled Neural Network (PCNN). Further, the propagation graph is encoded with negative sampling to obtain the embedding of deep propagation features between ODs. Meanwhile, we conduct deep network learning based on the Chengdu and NYC taxi datasets; our experimental evaluation results show it has a better performance compared to traditional feature construction methods.

Taxi travel time prediction based on fusion of traffic condition features

The research on travel time prediction has shown its importance and research significance in rational planning of travel arrangements and alleviating road congestion. As the level of urbanization increases, the number of cars has increased sharply. Traffic congestion has gradually become a norm in many first-tier and medium-sized cities. Existing models lack effective traffic information, especially traffic condition, to predict taxi travel time. The accuracy of the research model in urban areas is low. This will affect passenger travel arrangements. This paper defines urban congestion and taxi flow to represent traffic condition. A method for capturing traffic condition is proposed, and traffic condition and trajectory data are merged. Secondly, this work integrates two indirect factors of departure time and travel date to improve the accuracy of the model. The experimental results show that our model has some advantages over some classical models in travel time prediction.

Interday Stability of Taxi Travel Flow in Urban Areas

Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week’s data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week’s New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making.

Exploring the Spatio-Temporal and Behavioural Variations in Taxi Travel Based on Big Data during the COVID-19 Pandemic: A Case Study of New York City

The COVID-19 pandemic has caused severe social and economic chaos worldwide. To explore the impact of the COVID-19 pandemic on the travel patterns of residents, we analysed taxi trajectory data and COVID-19 pandemic data from New York City. Pearson coefficients, which were −0.7139, −0.8041, and −0.7046 during the three waves of the COVID-19, revealed a significant negative correlation between confirmed cases and taxi trips. Moran’s I was higher in drop-off areas than in pick-up areas, indicating a stronger spatial autocorrelation in drop-off areas during the study period. The hotspots of travel destinations had changed by spatial clustering, and variations in origin–destination distribution were obvious after the pandemic. Comparison of temporal and spatial dimensions before and after the pandemic revealed that strict epidemic policies directly affected travel. For instance, a week after the restrictions the taxi journeys plummeted by 95.3%, and their spatial and temporal patterns also changed. Once the anti-epidemic policy was eased or lifted, the taxi travel recovered, whereas, notably the new Omicron wave did not cause dramatic changes in taxi journeys. Despite this, travel spatial and temporal patterns did not return to pre-pandemic levels by the end of March 2022, the taxi journeys remained below half the pre-pandemic level. This study identified the profound impact of the COVID-19 outbreak on travel patterns and revealed distinct variations in behavioural responses during the pandemic and in response to subsequent policies. Strengthening targeted epidemic prevention and control measures are required to improve the balance between anti-epidemic policies and implementation efforts, that will facilitate the recovery of urban transport, work, and lifestyle of residents.

Forecasting and Planning Method for Taxi Travel Combining Carbon Emission and Revenue Factors-A Case Study in China.

The efficiency and emission levels of taxi operations are influenced by taxi drivers’ empirical judgments of hotspot travel areas. In this study, we exploited vehicle specific power (VSP) approaches and taxi trajectory data in a 1000 × 1000 m grid to calculate emission and revenue efficiency-related indicators and explored their spatial and temporal characteristics. Then, the entropy weight TOPSIS method was employed to identify the grids with the top comprehensive ranking of the indicators in the period to replace the driver experience. Finally, the k-means clustering method was utilized to identify the recommended road segments in the hotspot grid. The data from Nanchang City in China showed the following. (1) The study area was divided into 7553 grids, and the main travel and emission areas were located in the West Lake, Qingyunpu and Qingshan Lake districts (less than 200 grids). However, revenue efficiency-related indicators in this region are at a moderately low level. For example, the order revenue was about 0.9–1.2 RMB/min, and the average was 1.3–1.5 RMB/min. Areas with high trip demand had low revenue efficiency. (2) Five indicators related to emissions and revenue efficiency were selected. Of these, grid boarding points (G-bp) maintained the highest weight, reaching a maximum of 0.48 from 7:00 a.m. to 9:00 a.m. The ranking of secondary indicators was time varying. Hotspot grids and road segments were identified within each period. For example, from 1:00 a.m. to 3:00 a.m., (66,65), (68,65) were identified as hotspot grids. People’s Park North Gate near the road was identified as the recommended section from 1:00 a.m. to 3:00 a.m. This study can provide recommended grids and sections for idle cruising taxis.

Uncovering Taxi Mobility Patterns Associated with the Public Transportation Shutdown Using Multisource Data in Washington, D.C.

The relationship between taxi travel patterns and public transportation disruption has not been extensively explored. In this study, we investigated the impact of public transportation disruption on the taxi mobility patterns during the metro shutdown in Washington, D.C.. Multiple data source, involving taxi trips, traffic analysis zone, and point of interest (POI) information, was collected to compare the taxi travel patterns before, during, and after the metro shutdown. The number, distance, and duration of taxi trips were found to be significantly higher during the metro shutdown; specifically, the number of taxi trips was found to be 19.8% larger. Furthermore, a POI auxiliary analysis was performed to investigate the variation in community structure during the disruption of public transport using the modularity maximization approach. The results of this study will be useful for the development of taxi scheduling strategies and traffic management.

Prediction of Urban Taxi Travel Demand by Using Hybrid Dynamic Graph Convolutional Network Model

The efficient and accurate prediction of urban travel demand, which is a hot topic in intelligent transportation research, is challenging due to its complicated spatial-temporal dependencies, dynamic nature, and uneven distribution. Most existing forecasting methods merely considered the static spatial dependencies while ignoring the influence of the diversity of dynamic demand patterns and/or uneven distribution. In this paper, we propose a traffic demand forecasting framework of a hybrid dynamic graph convolutional network (HDGCN) model to deeply capture the characteristics of urban travel demand and improve prediction accuracy. In HDGCN, traffic flow similarity graphs are designed according to the dynamic nature of travel demand, and a dynamic graph sequence is generated according to time sequence. Then, the dynamic graph convolution module and the standard graph convolution module are introduced to extract the spatial features from dynamic graphs and static graphs, respectively. Finally, the spatial features of the two components are fused and combined with the gated recurrent unit (GRU) to learn the temporal features. The efficiency and accuracy of the HDGCN model in predicting urban taxi travel demand are verified by using the taxi data from Manhattan, New York City. The modeling and comparison results demonstrate that the HDGCN model can achieve stable and effective prediction for taxi travel demand compared with the state-of-the-art baseline models. The proposed model could be used for the real-time, accurate, and efficient travel demand prediction of urban taxi and other urban transportation systems.

Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China

Characterizing the taxi travel network is of fundamental importance to our understanding of urban mobility, and could provide intellectual support for urban planning, traffic congestion, and even the spread of diseases. However, the research on the interaction network between urban functional area (UFA) units are limited and worthy of notice. Therefore, this study has applied the taxi big data to construct a travel flow network for the exploration of spatial interaction relationships between different UFA units in Shenzhen, China. Our results suggested that taxi travel behavior was more active in UFA units dominated by functions, including residential, commercial, scenic, and greenspace during weekends, while more active in UFA units dominated by industrial function during weekdays. In terms of daily average volume, the characteristics of spatial interaction between the various UFA types during weekdays and weekends were similar. During the morning peak period, the sink areas were mainly distributed in Futian District and Nanshan District, while during the evening peak period, the sink areas were mainly distributed in the southern part of Yantian District, the southwestern part of Longgang District, and the eastern part of Luohu District. The average daily taxi mobility network during weekdays showed a spatial pattern of “dense in the west and north, sparse in the south and east”, exhibiting significant spatial unevenness. Compared with weekdays, the daily taxi mobility network during weekends was more dispersed and the differences in node sizes decreased, indicating that taxi travel destinations were more diverse. The pattern of communities was more consistent with the administrative division during weekdays, indicating that taxi trips are predominantly within the districts. Compared with weekdays, the community pattern of network during weekends was clearly different and more in line with the characteristics of a small world network. The findings can provide a better understanding of urban mobility characteristics in Shenzhen, and provide a reference for urban transportation planning and management.