Search arXivSearch

arXiv · 1711.02688

A cross-vendor and cross-state analysis of the GPS-probe data latency

Abstract

Crowdsourced GPS probe data has become a major source of real-time traffic information applications. In addition to traditional traveler advisory systems such as dynamic message signs (DMS) and 511 systems, probe data is being used for automatic incident detection, Integrated Corridor Management (ICM), end of queue warning systems, and mobility-related smartphone applications. Several private sector vendors offer minute by minute network-wide travel time and speed probe data. The quality of such data in terms of deviation of the reported travel time and speeds from ground-truth has been extensively studied in recent years, and as a result concerns over the accuracy of probe data has mostly faded away. However, the latency of probe data, defined as the lag between the time that disturbance in traffic speed is reported in the outsourced data feed, and the time that the traffic is perturbed, has become a subject of interest. The extent of latency of probe data for real-time applications is critical, so it is important to have a good understanding of the amount of latency and its influencing factors. This paper uses high-quality independent Bluetooth/Wi-Fi re-identification data collected on multiple freeway segments in three different states, to measure the latency of the vehicle probe data provided by three major vendors. The statistical distribution of the latency and its sensitivity to speed slowdown and recovery periods are discussed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhongxiang Wang, Masoud Hamedi, Elham Sharifi, Stanley Young. 2018-01-17. A cross-vendor and cross-state analysis of the GPS-probe data latency. https://doi.org/10.1177/0361198118792768

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

From Metrics to Decisions in NBA Analytics: A Critical Integrative Review and Decision-Readiness Framework

National Basketball Association (NBA) teams have increasingly detailed metrics, but better predictions do not necessarily improve decisions. This critical integrative review draws on prior reviews, citation tracing, and topic searches across seven research streams: on-court action, player value, role, lineup synergy, availability, draft and development, and contracts and roster construction. An observation-state-action-decision-evaluation chain organizes the synthesis. Six decision-readiness gates guide our assessment: point-in-time validity, uncertainty, context portability, action feasibility, opportunity-set observability, and evaluation, with requirements matched to each claim. The reviewed literature is strongest in measuring and predicting individual components of a decision. Evidence is less developed at interfaces that combine components, transfer them across settings, and compare feasible actions. We outline a proposed deployment workflow, a reporting contract, and a research agenda covering player transport, role substitution, roster fragility, legal action generation, and asset valuation. The 2023 collective bargaining agreement and forthcoming 3-2-1 Draft Lottery illustrate how institutional changes generate research questions. Models should inform evaluable comparisons of feasible choices. While its effect on organizational decision quality remains an empirical question, the framework provides a diagnostic and reporting structure for matching decision claims to evidence requirements.

stat.AP

Bayesian calibration of adaptive-behavior SIR models for multi-wave COVID-19 incidence in New York City

Epidemic incidence reflects both transmission dynamics and adaptive human behavior, yet these mechanisms may be difficult to distinguish from aggregate case data alone. We calibrated four susceptible--infected--recovered (SIR) specifications to weekly confirmed COVID-19 incidence in New York City from June to December 2020, comparing a single continuous SIR trajectory, a wave-initialized SIR model, and two adaptive-behavior models with either shared or wave-specific transmission. Inference was performed using rejection Approximate Bayesian Computation (ABC), and in-sample reconstruction was assessed using root mean squared error (RMSE) and the weighted interval score (WIS). Reinitializing the epidemic state by wave produced the largest structural improvement over the continuous SIR trajectory, reducing mean-based RMSE by 48.6\% and WIS by 17.6\%. Adding delayed prevalence-dependent behavioral adaptation with shared transmission further reduced mean-based RMSE by 23.4\%, but yielded essentially unchanged WIS relative to the wave-initialized SIR model. Allowing transmission to vary by wave did not provide a consistent additional advantage and produced strongly asymmetric posterior-simulation trajectories. Behavioral sensitivity, response midpoint, and delay remained only weakly to partially identified. The clearest posterior structure was a negative association between transmission intensity and the behavioral midpoint, indicating that higher transmission could be compensated by behavioral responses activated at lower prevalence. Sensitivity to ordered behavioral priors further showed that reconstruction and behavioral inference depend materially on structural prior assumptions. These results suggest that adaptive mechanisms can improve multi-wave incidence reconstruction, while aggregate incidence alone is insufficient to sharply separate transmission from behavioral adaptation.

stat.AP

Short-term rental market occupancy - daily time series for 2017-2022 on 500 markets worldwide

Short-term vacation rentals, as promoted by platforms such as Airbnb, Homeaway, Vrbo, etc., are a growing component of the travel industry. This paper provides a unique, large dataset on global market occupancy for the short-term rental market using data from the American company Wheelhouse. The dataset consists of data for $500$ markets around the world. For each market, a daily occupancy time series from January $2017$ to December $2022$ is provided, allowing for studies of local and global patterns in the evolution of the short-term rental market. Additionally, the dataset includes curves representing the booking trajectory of each market and stay date up to one year prior to the stay date. This large dataset comprises a unique combination of time series and survival analysis data, and is suitable as a methodological benchmark for both classical statistical and machine learning models.

stat.AP