Trip Data Research Articles

Data-driven micromobility network planning for demand and safety

Developing safe infrastructure for micromobility like bicycles or e-scooters is an efficient pathway towards climate-friendly, sustainable, and livable cities. However, urban micromobility infrastructure is typically planned ad-hoc and at best informed by survey data. Here, we study how data of micromobility trips and crashes can shape and automatize such network planning processes. We introduce a parameter that tunes the focus between demand-based and safety-based development, and investigate systematically this tradeoff for the city of Turin. We find that a full focus on demand or safety generates different network extensions in the short term, with an optimal tradeoff in-between. In the long term, our framework improves overall network quality independent of short-term focus. Thus, we show how a data-driven process can provide urban planners with automated assistance for variable short-term scenario planning while maintaining the long-term goal of a sustainable, city-spanning micromobility network.

Analysing hospital travel times in central Athens using Google Maps services

Abstract Introduction The importance of timely care is well documented for numerous emergency conditions, including STEMI and ischemic stroke, where low symptom-to-balloon/symptom-to-needle times are crucial for mortality and disability. The study of all potential delays helps us understand the constraints we have to work under. Here, we use Google Maps services to map the travel times from central Athens areas to on-duty hospitals Methods We built our code in the Python programming language, using the Google Maps Distance Matrix API to perform real-time trip duration calculations based on real-life data. As reference points, we used a set of Athens neighbourhoods provided by the Municipality as open data; we considered only public secondary and tertiary health facilities as valid destinations, and based our calculations based on the available daily duty schedules. Results Our algorithm collected 43,200 data points in total over two weeks, using 144 starting points. The average trip durations to reach an on-duty department formed a right-skewed distribution (-0.424), with a mean of 19.55 minutes and a median of 19.95 minutes. The maximum average time was 26.78 minutes, and the overall maximum was 44 minutes. Average travel times to cardiology, general surgery, neurology and internal medicine ERs, which experience a heavy patient load, were higher than the total mean (20.60/22.06/21.31/20.51 mins respectively). We found no correlation between the average travel time and average distance from a hospital or the geographical location, but we were able to create a map with hotspots of high/low travel times. Conclusions Our approach to collecting accurate trip data has shown the impact of time-of-day and location on trips to hospitals, even for patients within the same larger area. As acute care can be time-sensitive, similar wide-scale modelling could be used to create systemic solutions, e.g. data-guided spatial distribution of facilities or transportation. Key messages

Investigating the preferences between shared and non-shared ride-hailing services across user groups

We investigate the effects of travel duration and trip costs on mode choice between shared (e.g., Uber) and non-shared (e.g., the former Uber Pool) ride-hailing services. Using the City of Chicago as a case study, we employ a novel method by linking individual characteristics from the Chicago region’s household travel survey to trip characteristics from the City of Chicago’s ride-hailing trip data between November 2018 and December 2019. The findings from a binomial logit analysis and a latent class multinomial logit analysis indicate that travel duration and trip costs might not be the determinants for mode choice between shared and non-shared ride-hailing services. Under the current fare structure, some passengers might prefer one mode over the other regardless of the cost and time difference between the two types of services, while some might consider the differences in time and cost between the two modes not significant enough for switching mode. Our method allows us to investigate mode choice for the shared and non-shared ride-hailing services without relying on skim tables from regional travel models or stated preference surveys.

Planning for Bike Share Connectivity to Rail Transit

Bike sharing can play a role in providing access to transit stations and then to final destinations, but early implementation of these systems in North America has been opportunistic rather than strategic. This study evaluates local intermodal plan goals using trip data and associated infrastructure such as transit stops and bike share station locations in Austin, Texas, and Chicago, Illinois. Bike sharing use data from both cities suggest a weak relationship with existing rail stations that could be strengthened through collaborative, intermodal planning. The study suggests a planning framework and example language that could be tailored to help address the linkage between bike sharing and transit. Rather than an exhaustive study of the practice, this study provides evidence from these two cities that identify opportunities to improve intermodal planning. Cities that are planning or expanding a bike sharing system should consider carefully how to leverage this mode with existing modes of transport. Regardless of a city’s status in implementing a bike sharing system, planners can leverage information on existing transport systems for planning at regional and local levels.

Ride‐pooling in the light of COVID‐19: Determining spatiotemporal demand characteristics on the example of MOIA

AbstractThe mobility provider MOIA operates Europe's largest contiguous electric ride‐pooling service in Hamburg, representing a testbed of how shared and digitized transport can help foster the transformation of urban mobility. The on‐demand service has been in operation since 2019 and was thus affected by the COVID‐19 pandemic in 2020. This study shows real‐world insights into travel behavior before and during the pandemic, contributing to the empirical evidence on recent mobility behavior. After the application of descriptive statistical analyses, several (spatial) regression models are estimated to understand the relationship between spatial variables and demand. MOIA trip data from three different time periods are used: (a) before the COVID‐19 pandemic in summer and autumn 2019, (b) during the time of the first lockdown in Germany in spring 2020, and (c) after the first lockdown in summer and autumn 2020. A significant positive effect on ride‐pooling demand is observed for number of inhabitants, workplaces, gastronomic facilities, and at the airport in all time periods. In the course of the pandemic, the main travel patterns remained stable. However, the positive influences of gastronomy and the airport on ride‐pooling demand diminished in 2020. In contrast, the impact of hospitals on ride‐pooling demand increased in the course of the pandemic. In areas with high car ownership, ride‐pooling demand declined compared to pre‐pandemic times.

Spatial variation of ridesplitting adoption rate in Chicago

• Ridesplitting adoption rate (RAR) presents spatial heterogeneity across regions. • Factors affecting the RAR during different time periods are examined. • Subway station density is positive with the RAR in the southern area. • Potential substitutive and complementary effects between ridesplitting and transit. • Percent zero-vehicle households is negative with the RAR for most regions. Ridesplitting, a form of ride-hailing service where passengers with similar travel routes are matched to the same driver, can reduce the negative effects of solo ride-hailing trips and bring various environmental and social benefits. However, limited efforts were made to examine the spatial variation of ridesplitting trips, which was not conducive to the formulation of ridesplitting policies. To fill the gap, this work investigates the spatial variation of ridesplitting adoption rate (the proportion of ride-hailing trips with shared trip authorized, RAR) and its association with built environment and socio-economic factors at the census tract level, using the ride-hailing trip data in Chicago. To addressing the spatial heterogeneity, geographically weighted regression models are established to detect the factors influencing the RAR during different time periods, such as weekday, weekend, weekday morning peak and evening peak. Modeling results show that GWR models outperform the traditional global models in terms of model fit. The census tract level factors including subway station density, frequency of transit, land use mix, homicide density, percent female, the share of nonwhite, and percent zero-vehicle households have impacts on RAR, and the coefficient estimates of each explanatory variable vary across regions. The research results can help urban planners and transportation network companies develop refined policies to promote shared ride-hailing trips.

Electric Vehicle Trip Chain Information-Based Hierarchical Stochastic Energy Management With Multiple Uncertainties

This paper proposes a vehicle trip chain information-based hierarchical stochastic energy management system (Hi-SEMS) for a microgrid integrated with highly uncertain plug-in electric vehicles (PEVs) fleet, renewable energy sources, and loads. The two-layer Hi-SEMS scheme aims at optimal scheduling of PEV fleet to counteract the operational uncertainties encountered in real-time operation and minimizing the microgrid daily operating cost comprising the fuel cost and the emission cost. In the proposed work, the realistic behavior of PEVs fleet is modeled using a stochastic trip chain information to accurately forecast the charge/discharge demand taking into account the driver’s behavior as foundation. Realistic statistical trip data, multi-location charge/discharge of PEVs fleet to include coupled spatial-temporal dynamics of heterogeneous PEVs fleet is considered for intelligent charge/discharge scheduling. The effectiveness of the proposed strategy is verified on modified LV CIGRE and modified IEEE 33 bus radial distribution test networks and is compared with four different cases on each of the two test systems. The simulation results exhibit that the proposed Hi-SEMS outperforms the other four cases in terms of daily operating cost, the incentive to the PEVs owner, and robustness to multiple uncertainties.

Ride-hail to ride rail: Learning to balance supply and demand in ride-hailing services with intermodal mobility options

Growing spatiotemporal imbalance in supply and demand in ride-hailing services has been arousing concern. A variety of measures in the perspective of ride-hailing services per se is studied. However, approaching the imbalance problem in the broader perspective of intermodal mobility, which integrates different modes of passenger transportation in a single trip and aims to overcome limitations of any unimodal mobility, remains to be explored. This paper aims to investigate the potential of introducing intermodal mobility options to balance ride-hailing services. We first identify the importance of the availability decision on intermodal mobility options. Then we formulate the availability decision problem as a Markov decision process (MDP). Due to its convoluted system dynamics and large state space, we cast the intractable MDP into a reinforcement learning (RL) problem to approximately learn the availability policy. To stabilize the learning processes, we model the intermodal ride-hailing services as a stochastic queueing network and tailor a family of state-of-the-art RL algorithms to iteratively evaluate and improve the availability policy. Lastly, we test this optimization framework in a large-scale intermodal mobility scenario calibrated with real-world trip data. Results show that the learned availability policy can significantly dissipate riders’ queue and improve the service rates towards more balanced supply and demand.

Exploring the Spatially Heterogeneous Effects of the Built Environment on Bike-Sharing Usage during the COVID-19 Pandemic

Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study exploresspatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factros (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic,thus adjusting station locations, capacity, and rebalancing schemes accordingly.

Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design.

Traffic simulation is widely used for modeling, planning, and analyzing different strategies for traffic control and road development in a cost-efficient manner. In order to perform an intersection simulation, random vehicle trip data are typically applied to an intersection network, making them unrealistic. In this paper, we address this issue by presenting two different methods of incorporating actual turning movement count (TMC) data and comparing their similarity for intersection simulation and analysis. The TMC of three intersections in Las Vegas are estimated separately for one hour using a developed vision-based tracking system and they are incorporated into Simulation of Urban MObility (SUMO) for estimating traffic measurements and traffic signal design. t-tests with a 95% confidence interval on the simulation variables demonstrate the importance of using a route-based creation method which injects vehicles into a simulation environment based on the frame-level departure time. The intersection analyses and comparisons are performed based on estimated traffic measurements such as travel time, density, lane density, occupancy, and normalized waiting time. Since the critical edge of each intersection network is identified based on a higher normalized waiting time, new traffic signal designs are suggested based on the actual critical turning movements and improvements in vehicle travel time are achieved to better accommodate the actual traffic demand.

Temporal Reliability of Contingent Behavior Trip Data in Kuhn-Tucker Recreation Demand Models

Temporal Reliability of Contingent Behavior Trip Data in Kuhn-Tucker Recreation Demand Models

Availability analysis of shared bikes using abnormal trip data

The users’ cancelling rental data in the bike-sharing system (BSS) is usually regarded as abnormal trip data and is ignored. Abnormal trip data may have implicit information about the availability of shared bikes. So this paper presents an approach based on functional principal components analysis (FPCA) and clustering to advance the shared-bike availability analysis and maintenance strategy optimization using the abnormal trip data. In the proposed approach, the ratio of the cancelling rental number to the total rental number is scored as an index. Their values reflect a smooth variation in availability. The FPCA method is performed to explore the long-term availability variation modes of shared bikes. Then the dominant modes of availability variations are determined using the k-means algorithm. The effectiveness of the proposed approach is illustrated on the real-world trip data of a BSS. The analysis result indicates that the long-term availability level of the referred BSS has decreased from the initial 0.907 to 0.861. In the definite availability variation modes, the availability of one of the variation modes even has decreased to 0.709. Finally, the preventive maintenance model is presented to prevent the deterioration or availability decrease of shared bikes based on the mean functions of availability variation modes.

Air taxi skyport location problem with single-allocation choice-constrained elastic demand for airport access

Witnessing the accelerated commercialization efforts for air taxi services in across metropolitan cities, our research focuses on infrastructure planning of skyports. We consider design of skyport locations for air taxis accessing airports, where we present the skyport location problem as a modified single-allocation p-hub median location problem integrating choice-constrained user mode choice behavior. Our approach focuses on two alternative objectives i.e., maximizing air taxi ridership and maximizing air taxi revenue. The proposed models in the study incorporate trade-offs between trip length and trip cost based on mode choice behavior of travelers to determine optimal choices of skyports in a city. We examine the sensitivity of skyport locations based on two objectives, three air taxi pricing strategies, and varying transfer times at skyports. A case study of New York City is conducted considering a network of 149 taxi zones and 3 airports with over 20 million for-hire-vehicles trip data to the airports to discuss insights around the choice of skyport locations in the city, and demand allocation to different skyports under various parameter settings. Results suggest that a minimum of 9 skyports located between Manhattan, Queens and Brooklyn can adequately accommodate the airport access travel needs and are sufficiently stable against transfer time increases. Findings from this study can help air taxi providers strategize infrastructure design options and investment decisions based on skyport location choices.

How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation

Traffic analysis zone (TAZ) is the basic unit of urban transportation planning. The appropriateness of TAZ delineation will affect the rationality of transportation planning and the accuracy of transportation analysis, and thus affect the final planning decision-making. However, in the big data era, there is still a lack of methods to integrate multi-source data for TAZ delineation. How to effectively fuse multi-source heterogeneous data in the TAZ delineation process is still an unsolved technical difficulty. To fill this gap, this paper designs a multi-source data-driven stepwise strategy to solve the TAZ delineation problem by creating a zoning system with zones showing homogeneous mobility behaviors and containing homogeneous land use characteristics. Firstly, mining the spatial-temporal travel features and land use information of transit stations and parcels from multi-source data, including transit smart card data, ride-hailing data, bike-sharing trip data, and point-of-interest (POI) data. Then, a core parcel determination algorithm which mainly consists of Fuzzy C-Means station clustering and a constructed parcel core degree function is proposed to generate the core parcels of each potential TAZ. After that, parcels are clustered around the core ones into TAZs through a multi-feature driven clustering algorithm, in the process of which the homogeneity within TAZs is guaranteed. Finally, the optimal zoning system is obtained by comparing the information loss calculation result of multiple zoning schemes. Taking Beijing as a case study area, 624 TAZs are obtained by applying the proposed method. The delineation result is comparatively analyzed with TAZs obtained by the traditional delineation method and a baseline method by applying multi-indicator measurement. The result reveals that the presented approach can promote the rationality of TAZ delineation.

Minimizing fleet size and improving vehicle allocation of shared mobility under future uncertainty: A case study of bike sharing

As a rapidly expanding type of shared mobility, bike sharing is facing severe challenges of bike over-supply and demand fluctuation in many Chinese cities. In this paper, a large-scale method is developed to determine the minimum fleet size under future demand uncertainty, which is applied in a case study with millions of bike sharing trips in Nanjing. The findings show that if future uncertainty is not considered, more than 12% of trip demands may not be satisfied. Nevertheless, the proposed algorithm for minimizing fleet size based on historical trip data is effective in handling future uncertainty. For a bike sharing system, supplying 14.5% of the original fleet could be sufficient to meet 96.8% of trip demands. Meanwhile, the results suggest a unified platform that integrates multiple companies can significantly reduce the total fleet size by 44.6%. Moreover, in view of the Coronavirus Disease 2019 (COVID-19) pandemic, this paper proposes a contact delay policy that maintains a suitable usage interval, which results in increased bike amount requirements. These findings provide useful insights for improving resource efficiency and operational services in shared mobility applications.

Impacts of the COVID-19 pandemic on the spatio-temporal characteristics of a bicycle-sharing system: A case study of Pun Pun, Bangkok, Thailand.

The COVID-19 pandemic is found to be one of the external stimuli that greatly affects mobility of people, leading to a shift of transportation modes towards private individual ones. To properly explain the change in people's transport behavior, especially in pre- and post- pandemic periods, a tensor-based framework is herein proposed and applied to Pun Pun-the only public bicycle-sharing system in Bangkok, Thailand-where multidimensional trip data of Pun Pun are decomposed into four different modes related to their spatial and temporal dimensions by a non-negative Tucker decomposition approach. According to our computational results, the first pandemic wave has a sizable influence not only on Pun Pun but also on other modes of transportation. Nonetheless, Pun Pun is relatively more resilient, as it recovers more quickly than other public transportation modes. In terms of trip patterns, we find that, prior to the pandemic, trips made during weekdays are dominated by business trips with two peak periods (morning and evening peaks), while those made during weekends are more related to leisure activities as they involve stations nearby a public park. However, after the first pandemic wave ends, the patterns of weekday trips have been drastically changed, as the number of business trips sharply drops, while that of educational trips connecting metro/subway stations with a major educational institute in the region significantly rises. These findings may be regarded as a reflection of the ever-changing transport behavior of people seeking a sustainable mode of private transport, with a more positive outlook on the use of bicycle-sharing system in Bangkok, Thailand.

A matrix factorization model with local and global consistency for flow prediction in bike-sharing systems

With the increasing concerns about the environmental impact of motor vehicles, more cities are investing in Bike-Sharing Systems (BSSs) as an alternative mode of transport for their citizens. In such systems, predicting the fine-grained station-level bike-flow improves the operation and reliability. Some recent studies have employed graph-based approaches to model BSSs, however, considering spatial closeness and communities in the flow prediction has not been fully addressed yet. In this paper, we propose a Matrix Factorization model with Local and Global consistency (MFLOG) to be used for flow prediction in BSSs. MFLOG captures the dynamics and underlying structure of a BSS and models spatial closeness, temporal variations, and communities in a BSS. We also investigate the relationship between spatial closeness and bike-flow and explore the stability of communities in the BSS. The proposed method is evaluated on the Divvy Trips data set in the City of Chicago. The results show that the MFLOG model improves the accuracy of single and multiple-step bike-flow and check-in/out predictions over the baseline models.

A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem

Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing. For this reason, operators tend to employ simplified algorithms that have been demonstrated to work well in a particular setting. To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost. In particular, by treating dispatch as part of the environment dynamics, a centralized agent can learn to intermittently direct the dispatcher to reposition free vehicles and mitigate against fleet imbalance. We formulate RL state and action spaces as distributions over a grid partitioning of the operating area, making the framework scalable and avoiding the complexities associated with multiagent RL. Numerical experiments, using real-world trip and network data, demonstrate that this approach has several distinct advantages over baseline methods including: improved system cost; high degree of adaptability to the selected dispatch method; and the ability to perform scale-invariant transfer learning between problem instances with similar vehicle and request distributions.

Optimization-based trip chain emulation for electrified ride-sourcing charging demand analyses

ABSTRACT Range anxiety remains one of the key concerns for ride-sourcing drivers to adopt battery electric vehicles (BEVs). To investigate the feasibility of using BEVs for ride-sourcing services, we propose an optimization-based methodology to estimate the daily driving trip patterns of ride-sourcing vehicles based on widely available non-identifiable trip data. Furthermore, we investigate the charging needs of electrified ride-sourcing vehicles using agent-based simulation. The methodologies are illustrated through a case study in the city of Chicago. Through sensitivity analysis on driver working hours and initial charging status, we quantify the range of daily average vehicle miles traveled (VMT) per car and identify the hot spots of current public charging demand and potential unsatisfied charging demand. This study can be used to determine the priorities of future charging infrastructure investment to further mitigate range anxiety and promote adoption of electrified ride-sourcing services.

Connecting metros with shared electric scooters: Comparisons with shared bikes and taxis

The rapid rise of shared electric scooter (E-Scooter) systems offers urban areas a new micro-mobility solution. The focus on short-distance travel has made it a competitive option for addressing first-/last-mile travel needs. Nevertheless, its role as a first-/last-mile solution was understudied due to the lack of fine-grained trip data. This study aims at exploring the integration of shared E-Scooters with public transportation systems. Specifically, it compared the use of shared E-Scooters against shared bikes and taxis for connecting trips from/to metro stations. We analyzed massive amounts of trip-related data extracted through APIs. Multinomial logistic regression models were developed to uncover how the mode choices from/to metro stations vary in different contexts. The results show that the use of shared E-Scooters to connect trips from/to metro stations can be notably different from shared bikes and taxis. The preference of shared E-Scooters will vary depending on the land use and time period.