Taxi Trip Data Research Articles

TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data

Travel behavior understanding is a long-standing and critically important topic in the area of smart cities. Big volumes of various GPS-based travel data can be easily collected, among which the taxi GPS trajectory data is a typical example. However, in GPS trajectory data, there is usually little information on travelers’ activities, thereby they can only support limited applications. Quite a few studies have been focused on enriching the semantic meaning for raw data, such as travel mode/purpose inferring. Unfortunately, trip purpose imputation receives relatively less attention and requires no real-time response. To narrow the gap, we propose a probabilistic two-phase framework named TripImputor , for making the real-time taxi trip purpose imputation and recommending services to passengers at their dropoff points. Specifically, in the first phase, we propose a two-stage clustering algorithm to identify candidate activity areas (CAAs) in the urban space. Then, we extract fine-granularity spatial and temporal patterns of human behaviors inside the CAAs from foursquare check-in data to approximate the priori probability for each activity, and compute the posterior probabilities (i.e., infer the trip purposes) using Bayes’ theorem. In the second phase, we take a sophisticated procedure that clusters historical dropoff points and matches the dropoff clusters and CAAs to immerse the real-time response. Finally, we evaluate the effectiveness and efficiency of the proposed two-phase framework using real-world data sets, which consist of road network, check-in data generated by over 38 000 users in one year, and the large-scale taxi trip data generated by over 19 000 taxis in a month in Manhattan, New York City, USA. Experimental results demonstrate that the system is able to infer the trip purpose accurately, and can provide recommendation results to passengers within 1.6 s in Manhattan on average, just using a single normal PC.

Improving crime count forecasts using Twitter and taxi data

Crime prediction is crucial to criminal justice decision makers and efforts to prevent crime. The paper evaluates the explanatory and predictive value of human activity patterns derived from taxi trip, Twitter and Foursquare data. Analysis of a six-month period of crime data for New York City shows that these data sources improve predictive accuracy for property crime by 19% compared to using only demographic data. This effect is strongest when the novel features are used together, yielding new insights into crime prediction. Notably and in line with social disorganisation theory, the novel features cannot improve predictions for violent crimes.

A Stigmergy-Based Analysis of City Hotspots to Discover Trends and Anomalies in Urban Transportation Usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.

Cost, Energy, and Environmental Impact of Automated Electric Taxi Fleets in Manhattan.

Shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions. We estimate that costs will be lowest with a battery range of 50-90 mi, with either 66 chargers per square mile, rated at 11 kW or 44 chargers per square mile, rated at 22 kW. We estimate that the cost of service provided by such an SAEV fleet will be $0.29-$0.61 per revenue mile, an order of magnitude lower than the cost of service of present-day Manhattan taxis and $0.05-$0.08/mi lower than that of an automated fleet composed of any currently available hybrid or internal combustion engine vehicle (ICEV). We estimate that such an SAEV fleet drawing power from the current NYC power grid would reduce GHG emissions by 73% and energy consumption by 58% compared to an automated fleet of ICEVs.

Improving Crime Count Forecasts Using Twitter and Taxi Data

Data from social media has created opportunities to understand how and why people move through their urban environment and how this relates to criminal activity. To aid resource allocation decisions in the scope of predictive policing, the paper proposes an approach to predict weekly crime counts. The novel approach captures spatial dependency of criminal activity through approximating human dynamics. It integrates point of interest data in the form of Foursquare venues with Twitter activity and taxi trip data, and introduces a set of approaches to create features from these data sources. Empirical results demonstrate the explanatory and predictive power of the novel features. Analysis of a six-month period of real-world crime data for the city of New York evidences that both temporal and static features are necessary to effectively account for human dynamics and predict crime counts accurately. Furthermore, results provide new evidence into the underlying mechanisms of crime and give implications for crime analysis and intervention.

A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends

Massive flows that represent the individual level of movements and communications can be easily obtained in the age of big data. Generalizing spatial and temporal flow patterns from such data is essential to demonstrate spatial connections and mobility trends. Clustering approaches provide effective methods to handle data sets that contain massive individual-level flows. However, existing flow clustering studies obscure the geometric properties of flow data, such as direction and length, which significantly indicate movement trends. In addition, temporal information is often ignored because previous approaches have mainly focused on the perspective of spatial clusters of flow data, resulting in a loss of temporal patterns. In this paper, we introduce new spatial and temporal similarity measurements between flows and propose a new clustering approach of flow data based on a stepwise strategy. This method can identify clusters from distinct flow distributions and discover significant spatio-temporal trends from large flow data. Simulated experiments with synthetic flows and a case study using Beijing taxi trip data are conducted to validate the usefulness of the proposed method.

DISPAQ: Distributed Profitable-Area Query from Big Taxi Trip Data.

One of the crucial problems for taxi drivers is to efficiently locate passengers in order to increase profits. The rapid advancement and ubiquitous penetration of Internet of Things (IoT) technology into transportation industries enables us to provide taxi drivers with locations that have more potential passengers (more profitable areas) by analyzing and querying taxi trip data. In this paper, we propose a query processing system, called Distributed Profitable-Area Query (DISPAQ) which efficiently identifies profitable areas by exploiting the Apache Software Foundation’s Spark framework and a MongoDB database. DISPAQ first maintains a profitable-area query index (PQ-index) by extracting area summaries and route summaries from raw taxi trip data. It then identifies candidate profitable areas by searching the PQ-index during query processing. Then, it exploits a Z-Skyline algorithm, which is an extension of skyline processing with a Z-order space filling curve, to quickly refine the candidate profitable areas. To improve the performance of distributed query processing, we also propose local Z-Skyline optimization, which reduces the number of dominant tests by distributing killer profitable areas to each cluster node. Through extensive evaluation with real datasets, we demonstrate that our DISPAQ system provides a scalable and efficient solution for processing profitable-area queries from huge amounts of big taxi trip data.

LABELING RESIDENTIAL COMMUNITY CHARACTERISTICS FROM COLLECTIVE ACTIVITY PATTERNS USING TAXI TRIP DATA

Abstract. There existing a significant social and spatial differentiation in the residential communities in urban city. People live in different places have different socioeconomic background, resulting in various geographically activity patterns. This paper aims to label the characteristics of residential communities in a city using collective activity patterns derived from taxi trip data. Specifically, we first present a method to allocate the O/D (Origin/Destination) points of taxi trips to the land use parcels where the activities taken place in. Then several indices are employed to describe the collective activity patterns, including both activity intensity, travel distance, travel time, and activity space of residents by taking account of the geographical distribution of all O/Ds of the taxi trip related to that residential community. Followed by that, an agglomerative hierarchical clustering algorithm is introduced to cluster the residential communities with similar activity patterns. In the case study of Wuhan, the residential communities are clearly divided into eight clusters, which could be labelled as ordinary communities, privileged communities, old isolated communities, suburban communities, and so on. In this paper, we provide a new perspective to label the land use under same type from people’s mobility patterns with the support of big trajectory data.

Optimal assignment and incentive design in the taxi group ride problem

Taxi group ride (TGR) is one popular case of taxi ridesharing, where passenger trips with nearby origins and destinations and similar departure time are grouped into a single ride. The study investigates theoretical and practical aspects of TGR implementation in real world. In particular, two essential problems on operation strategy and policy making of TGR are examined. First, we investigate the optimal assignment of a set of passengers for the sake of maximizing total saved travel miles. Second, we analyze different behaviors of passengers and drivers in participating taxi group rides, and explore the best incentives for TGR in order to maximize efficiency under optimal assignment. The optimal assignment is formulated as an integer linear programming problem and is further converted into an equivalent graph problem. While the problem is NP-hard, efficient algorithms are needed for real-world on-line implementations. We develop an exact algorithm and a heuristic algorithm to solve the TGR problem, and compare the results with a bounded-error greedy algorithm. The numerical experiments suggest that the heuristic algorithm is capable of solving real-world TGR instances efficiently with good solution quality. To explore the best incentives for grouped taxi rides, comprehensive numerical experiments are conducted using taxi trip data from New York City (US), Wuhan (China), and Shenzhen (China). Our numerical results show that over 47% of the total taxi trip mileage may be saved if proper level of incentives are provided and if passengers are matched optimally.

Use of Taxi-Trip Data in Analysis of Demand Patterns for Detection and Explanation of Anomalies

Because of environmental and economic stress, current strong investment in adaptive transport systems can efficiently use capacity, minimizing costs and environmental impacts. The common vision is of a system that dynamically changes itself (the supply) to anticipate the needs of travelers (the demand). In some occasions, unexpected and unwanted demand patterns are noticed in the traffic network; these patterns lead to system failures and cost implications. Significantly, low speeds or excessively low flows at an unforeseeable time are only some of the phenomena that are often noticed and need to be explained for a transport system to develop a better future response. The objective of this research was the formulation of a methodology that could identify anomalies on traffic networks and correlate them with special events by using Internet data. The main subject of interest in this study was the investigation of why traffic congestion was occurring as well as why demand fluctuated on days when there were no apparent reasons for such phenomena. The system was evaluated by using Google’s public data set for taxi trips in New York City. A “normality” baseline was defined at the outset and then used in the subsequent study of the demand patterns of individual days to detect outliers. With the use of this approach it was possible to detect fluctuations in demand and to analyze and correlate them with disruptive event scenarios such as extreme weather conditions, public holidays, religious festivities, and parades. Kernel density analysis was used so that the affected areas, as well as the significance of the observed differences compared with the average day, could be depicted.

A Graph-Based Approach to Measuring the Efficiency of an Urban Taxi Service System

Taxi service systems in big cities are immensely complex due to the interaction and self-organization between taxi drivers and passengers. An inefficient taxi service system leads to more empty trips for drivers and longer waiting time for passengers and introduces unnecessary congestion on the road network. In this paper, we investigate the efficiency level of the taxi service system using real-world large-scale taxi trip data. By assuming a hypothetical system-wide recommendation system, two approaches are proposed to find the theoretical optimal strategies that minimize the cost of empty trips and the number of taxis required to satisfy all the observed trips. The optimization problems are transformed into equivalent graph problems and solved using polynomial time algorithms. The taxi trip data in New York City are used to quantitatively examine the gap between the current system performance and the theoretically optimal system. The numerical results indicate that, if system-wide information between taxi drivers and passengers was shared, it is possible to reduce 60%–90% of the total empty trip cost depending on different objectives, and one-third of all taxis required to serve all observed trips. The existence of destructive competition among taxi drivers is also uncovered in the actual taxi service system. The huge performance gap suggests an urgent need for a system reconsideration in designing taxi recommendation systems.

Analysis of Grid Cell–Based Taxi Ridership with Large-Scale GPS Data

Understanding the spatial variation of taxi ridership is of critical importance to many government agencies and taxi companies because taxis’ location dependency on spatial pattern of passenger demand results in spatially unbalanced taxi demand and supply. This study presents an analysis of the spatial distribution of taxi ridership by using large-scale GPS taxi trip data collected from Seoul, South Korea. To capture the spatial variations better in taxi ridership, GPS entities were disaggregated into units of a uniform size with a grid cell decomposition method. A geographically weighted spatial regression was applied to model spatial correlations of factors associated with transit and urban density to taxi ridership. Results from the proposed method demonstrated a higher relationship between taxi and subway ridership in the regions where lower accessibility to subway stations existed. In these regions, taxis were found to perform as a complementary mode to subway. In residential and commercial districts, this analysis showed that population and employment were highly related to taxi ridership. In contrast, in central business districts it was the building area (floor space), rather than population and employment, that was highly related to taxi ridership.

Detecting Urban Dynamics with Taxi Trip Data for Evaluation and Optimizing of Spatial Planning:

Detecting Urban Dynamics with Taxi Trip Data for Evaluation and Optimizing of Spatial Planning:

Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco

In this study, we present exploratory evidence of how “ridesourcing” services (app-based, on-demand ride services like Uber and Lyft) are used in San Francisco. We explore who uses ridesourcing and for what reasons, how the ridesourcing market compares to that of traditional taxis, and how ridesourcing impacts the use of public transit and overall vehicle travel. In spring 2014, 380 completed intercept surveys were collected from three ridesourcing “hot spots” in San Francisco. We compare survey results with matched-pair taxi trip data and results of a previous taxi user survey. We also compare travel times for ridesourcing and taxis with those for public transit. The findings indicate that, despite many similarities, taxis and ridesourcing differ in user characteristics, wait times, and trips served. While ridesourcing replaces taxi trips, at least half of ridesourcing trips replaced modes other than taxi, including public transit and driving. Impacts on overall vehicle travel are unclear. We conclude with suggestions for future research.

Incorporating spatial interaction patterns in classifying and understanding urban land use

ABSTRACTLand use classification has benefited from the emerging big data, such as mobile phone records and taxi trajectories. Temporal activity variations derived from these data have been used to interpret and understand the land use of parcels from the perspective of social functions, complementing the outcome of traditional remote sensing methods. However, spatial interaction patterns between parcels, which could depict land uses from a perspective of connections, have rarely been examined and analysed. To leverage spatial interaction information contained in the above-mentioned massive data sets, we propose a novel unsupervised land use classification method with a new type of place signature. Based on the observation that spatial interaction patterns between places of two specific land uses are similar, the new place signature improves land use classification by trading off between aggregated temporal activity variations and detailed spatial interactions among places. The method is validated with a case study using taxi trip data from Shanghai.

Revealing travel patterns and city structure with taxi trip data

Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of taxi-trip data from Shanghai, we built spatially embedded networks to model intra-city spatial interactions and to introduce network science methods into the analysis. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long- and short-distance trips, we reveal a two-level hierarchical polycentric city structure in Shanghai. Further explorations of sub-network structures demonstrate that urban sub-regions have broader internal spatial interactions, while suburban centers are more influential on local traffic. By incorporating the land use of centers from a travel-pattern perspective, we investigate sub-region formation and the interaction patterns of center–local places. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in developing and applying urban transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than for other structures, such as administrative divisions.

Parallel online spatial and temporal aggregations on multi-core CPUs and many-core GPUs

With the increasing availability of locating and navigation technologies on portable wireless devices, huge amounts of location data are being captured at ever growing rates. Spatial and temporal aggregations in an Online Analytical Processing (OLAP) setting for the large-scale ubiquitous urban sensing data play an important role in understanding urban dynamics and facilitating decision making. Unfortunately, existing spatial, temporal and spatiotemporal OLAP techniques are mostly based on traditional computing frameworks, i.e., disk-resident systems on uniprocessors based on serial algorithms, which makes them incapable of handling large-scale data on parallel hardware architectures that have already been equipped with commodity computers. In this study, we report our designs, implementations and experiments on developing a data management platform and a set of parallel techniques to support high-performance online spatial and temporal aggregations on multi-core CPUs and many-core Graphics Processing Units (GPUs). Our experiment results show that we are able to spatially associate nearly 170 million taxi pickup location points with their nearest street segments among 147,011 candidates in about 5–25s on both an Nvidia Quadro 6000 GPU device and dual Intel Xeon E5405 quad-core CPUs when their Vector Processing Units (VPUs) are utilized for computing intensive tasks. After spatially associating points with road segments, spatial, temporal and spatiotemporal aggregations are reduced to relational aggregations and can be processed in the order of a fraction of a second on both GPUs and multi-core CPUs. In addition to demonstrating the feasibility of building a high-performance OLAP system for processing large-scale taxi trip data for real-time, interactive data explorations, our work also opens the paths to achieving even higher OLAP query efficiency for large-scale applications through integrating domain-specific data management platforms, novel parallel data structures and algorithm designs, and hardware architecture friendly implementations.