Exploring Spatial Distribution of Urban Park Service Areas in Shanghai Based on Travel Time Estimation: A Method Combining Multi-Source Data

Due to a growing appreciation for the ecological and recreational benefits of public green spaces, the evaluation of urban parks’ service efficiency, as well as citizens’ behavioral preferences for daily recreation, have become an increasing academic focus. However, due to the lack of empirical approaches, existing research on exploring park service areas has been simplified by their use of Euclidean distance or buffer sets by simulation, ignoring the fact that the likelihood of citizens visiting urban parks is time sensitive. Utilizing mobile signaling data and web map services, this study proposes an approach to estimating the travel times of park visitors and analyzing the characteristics of park service areas from the perspective of actual time consumption. Taking Shanghai as a case study, this research firstly identified the time–cost decay of parks with different areas and locations. A comparison analysis was then used to examine the spatial relationship between park service areas and their accessibility defined by time consumption. The results show that (1) urban parks in Shanghai have larger mean service radii than existing planning guidelines, and park service areas were significantly influenced by park locations; (2) people have a great preference for urban parks whose travel times by public transit are under 40 min, and they have no desire to visit parks located within or outside the Middle Ring Road when the travel times reach 60 min and 75 min, respectively; (3) the shapes of park service areas are consistent with the high-accessibility districts defined by time thresholds, in spite of some differences caused by citizens’ choices. These findings provide an effective tool for evaluating the actual characteristics of park recreational services, along with direct implications for policymakers aiming to establish effective strategies for improving the accessibility and vitality of urban parks.

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Travel time estimation in urban networks using limited probes data

Canadian Journal of Civil Engineering ◽

10.1139/l11-001 ◽

2011 ◽

Vol 38 (3) ◽

pp. 305-318 ◽

Cited By ~ 10

Author(s):

Mohamed El Esawey ◽

Tarek Sayed

Keyword(s):

Simulation Model ◽

Travel Time ◽

Network Performance ◽

Time Estimation ◽

Performance Measure ◽

Estimation Accuracy ◽

Travel Time Estimation ◽

Travel Times ◽

Time Data ◽

Travel Time Data

Travel time is a simple and robust network performance measure that is well understood by the public. However, travel time data collection can be costly especially if the analysis area is large. This research proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. Within a homogeneous road network, nearby links of similar character are exposed to comparable traffic conditions, and therefore, their travel times are likely to be positively correlated. This correlation can be useful in developing travel time relationships between nearby links so that if data becomes available on a subset of these links, travel times of their neighbours can be estimated. A methodology is proposed to estimate link travel times using available data from neighbouring links. To test the proposed methodology, a case study was undertaken using a VISSIM micro-simulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. Neighbour links travel time estimation accuracy was assessed using different error measurements and the results were satisfactory. Overall, the results of this research demonstrate the feasibility of using neighbour links data as an additional source of information to estimate travel time, especially in case of limited coverage.

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Travel-time errors in the laterally inhomogeneous earth

Bulletin of the Seismological Society of America ◽

10.1785/bssa0610061639 ◽

1971 ◽

Vol 61 (6) ◽

pp. 1639-1654 ◽

Cited By ~ 2

Author(s):

Cinna Lomnitz

Keyword(s):

Travel Time ◽

Root Mean Square ◽

Time Estimation ◽

Systematic Errors ◽

Least Square ◽

Travel Time Estimation ◽

Mean Square ◽

Travel Times ◽

The Earth ◽

Lateral Inhomogeneity

abstract Travel times from earthquakes or explosions contain both positive and negative systematic errors. Positive skews in travel-time residuals due to epicenter mislocation, and negative skews due to lateral inhomogeneity in the Earth, are analyzed. Methods for travel-time estimation are critically reviewed. Recent travel-time tables, including the J-B tables, are within the range of root-mean-square travel-time fluctuations; the J-B tables are systematically late but cannot be reliably improved by least-square methods. Effects of lateral inhomogeneity at teleseismic distances can be estimated by chronoidal methods independently of standard tables, but the available explosion data are insufficiently well-distributed in azimuth and distance for this purpose.

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Investigation of Bicycle Travel Time Estimation Using Bluetooth Sensors for Low Sampling Rates

PROMET - Traffic&Transportation ◽

10.7307/ptt.v26i5.1343 ◽

2014 ◽

Vol 26 (5) ◽

pp. 383-391 ◽

Cited By ~ 2

Author(s):

Zhenyu Mei ◽

Dianhai Wang ◽

Jun Chen ◽

Wei Wang

Keyword(s):

Travel Time ◽

Time Estimation ◽

Travel Time Estimation ◽

Frequency Noise ◽

Multiple Time ◽

Travel Times ◽

Data Filtering ◽

Valid Data ◽

Sampling Intervals ◽

Filtering Procedure

Filtering the data for bicycle travel time using Bluetooth sensors is crucial to the estimation of link travel times on a corridor. The current paper describes an adaptive filtering algorithm for estimating bicycle travel times using Bluetooth data, with consideration of low sampling rates. The data for bicycle travel time using Bluetooth sensors has two characteristics. First, the bicycle flow contains stable and unstable conditions. Second, the collected data have low sampling rates (less than 1%). To avoid erroneous inference, filters are introduced to “purify” multiple time series. The valid data are identified within a dynamically varying validity window with the use of a robust data-filtering procedure. The size of the validity window varies based on the number of preceding sampling intervals without a Bluetooth record. Applications of the proposed algorithm to the dataset from Genshan East Road and Moganshan Road in Hangzhou demonstrate its ability to track typical variations in bicycle travel time efficiently, while suppressing high frequency noise signals.

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Comparison of Door-to-Door Transit Travel Time Estimation Using Schedules, Real-Time Vehicle Arrivals, and Smartcard Inference Methods

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211023768 ◽

2021 ◽

pp. 036119812110237

Author(s):

Ting Li ◽

Patrick Meredith-Karam ◽

Hui Kong ◽

Anson Stewart ◽

John P. Attanucci ◽

...

Keyword(s):

Travel Time ◽

Optimal Path ◽

Time Estimation ◽

Future Research ◽

Travel Time Estimation ◽

Travel Times ◽

Transit Systems ◽

Time Estimates ◽

Bus Service ◽

Transportation Research

Estimating passengers’ door-to-door travel time, for journeys that combine walking and public transit, can be complex for large networks with many available path alternatives. Additional complications arise in tap-on only transit systems, where passengers alightings are not recorded. For one such system, the Chicago Transit Authority, this study compares three methods for estimating door-to-door travel time: assuming optimal path choice given scheduled service, as represented in the General Transit Feed Specification (GTFS); assuming optimal path choice given actually operated bus service, as recorded by automatic vehicle location systems; and using inferred path choices based on automated fare collection smartcard records, as processed with an origin-destination-interchange (ODX) inference algorithm. As expected, ODX-derived travel times are found to be longer than those derived from GTFS, indicating that purely schedule-based travel times underestimate the travel times that are actually available and experienced, which can be attributed primarily to suboptimal passenger route choice. These discrepancies additionally manifest in significant spatial variations, raising concerns about potential biases in travel time estimates that do not account for reliability. The findings bring about a more comprehensive understanding of the interactions between transit reliability and passenger behavior in transportation research. Furthermore, these discrepancies suggest areas of future research into the implications of systematic and behavioral assumptions implied by using conventional schedule-based travel time estimates.

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A ROBUST METHOD FOR REAL TIME ESTIMATION OF TRAVEL TIMES FOR DENSE URBAN ROAD NETWORKS USING POINT-TO-POINT DETECTORS

Transport ◽

10.3846/16484142.2015.1078845 ◽

2015 ◽

Vol 30 (3) ◽

pp. 264-272 ◽

Cited By ~ 12

Author(s):

Evangelos Mitsakis ◽

Josep Maria Salanova Grau ◽

Evangelia Chrysohoou ◽

Georgia Aifadopoulou

Keyword(s):

Real Time ◽

Road Networks ◽

Central Business District ◽

Time Estimation ◽

Media Access Control ◽

Travel Time Estimation ◽

Travel Times ◽

Error Sources ◽

Urban Road ◽

Point To Point

Data collection for the provision of real time traveller information services is a key issue, both for the travellers as well as for traffic managers. This paper presents a methodology for estimating travel times in dense urban road networks using point-to-point detectors. The aim is to fill in the gap of existing travel time estimation methodologies, which are based on point-to-point detection devices. Bluetooth (BT) is considered as one of the less expensive technologies for estimating travel times. Data filtering and data correction require rigorous methodologies, which if not correctly applied may result in inaccurate results as compared to other methods. The main difficulty of data processing is to identify the correct set of Media Access Control (MAC) addresses for estimating travel times, especially in dense urban road networks, where three main error sources exist: the co-existence of various transport modes (private vehicles, buses, pedestrians, bicycles etc.), the existence of more than one possible paths between two BT detectors and the existence of stops or trips ending between two BT detectors. These error sources create outliers that need to be identified and taken into account. The results of the proposed methodology confirm that outliers are eliminated, as shown by a case study with 10 BT detectors installed at major intersections of Thessaloniki’s Central Business District (CBD).

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Aggregate- and Disaggregate-Based Travel Time Estimations: Comparison of Applications to Sustainability Analysis and Advanced Traveler Information Systems

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1725-12 ◽

2000 ◽

Vol 1725 (1) ◽

pp. 86-94 ◽

Cited By ~ 4

Author(s):

Josias Zietsman ◽

Laurence R. Rilett

Keyword(s):

Information Systems ◽

Travel Time ◽

Time Estimation ◽

Travel Time Estimation ◽

Travel Times ◽

Advanced Traveler Information Systems ◽

Traveler Information ◽

Traveler Information Systems ◽

Sustainability Analysis ◽

Wide Range

Travel time estimation is important for a wide range of applications, including advanced traveler information systems (ATIS), sustainability analysis, and discrete choice modeling. Approaches to travel time estimation traditionally have been based on aggregate data sets that examine travel times over a number of days or travel times in previous time intervals. Automatic vehicle identification data make it possible to analyze travel time data at a totally disaggregate or individual commuter level. It is postulated in this research that the capability of modeling travel characteristics on a disaggregate level can improve the accuracy with which performance measures are quantified. The test beds examined are a 22-km section of the I-10 corridor and a 21-km section of the US-290 corridor in Houston, Texas. It was found that aggregation across days, which does not consider the effect of individual days, is 63 percent less accurate than aggregation by days, which does consider the effect of individual days. Even though the latter technique was found to be more accurate, it was illustrated that 40 percent of the regular commuters’ travel times are statistically different from these aggregate estimates. Similarly, for travel time variability, it was found that for approximately 20 percent of the cases the travel time standard deviations for regular commuters are statistically different from the aggregate estimates. These results illustrate the uniqueness of an individual commuter’s travel patterns and emphasize the benefit of conducting analyses at the level of the individual commuter for both ATIS and sustainable transportation.

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