scholarly journals From Massive Trajectory Data to Traffic Modeling for Better Behavior Prediction in a Usage-Based Insurance Context

2020 ◽  
Vol 9 (12) ◽  
pp. 722
Author(s):  
Philippe Blais ◽  
Thierry Badard ◽  
Thierry Duchesne ◽  
Marie-Pier Côté
Keyword(s):  
Travel Time ◽  
Average Length ◽  
Contextual Information ◽  
Explanatory Power ◽  
Large Data ◽  
Insurance Companies ◽  
Trajectory Data ◽  
Google Maps ◽  
The Difference ◽  
Link Travel Time

Usage-Based Insurance (UBI) is an insurance framework that has made its appearance in the last few years. It allows direct measurement of the traveling of policyholders, hence the growing interest of the industry to better understand driving behaviors. UBI generates large data volumes, from which events can be extracted, like harsh brakes or accelerations. Still, these events are measured without contextual information, which limits their explanatory power. Traffic is one of these types of contextual information that may have great potential, but access to such data remains an issue. Solutions exist, like traffic data from external providers, but for insurance companies that conduct business over large areas, this could result in very large costs. This paper demonstrates that data from insurance companies acquired via UBI can be used to model traffic. A method based on link travel time is proposed and tested on four Canadian cities. Then, routes created with the model are compared with those created using Google Maps. The results show that for 38 routes with an average length of around 5 km, the difference between the travel time of the routes of the proposed model and Google Maps is as small as one second on average.

Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

2022 ◽  
Vol 13 (1) ◽  
pp. 1-18
Author(s):  
Meng Chen ◽  
Qingjie Liu ◽  
Weiming Huang ◽  
Teng Zhang ◽  
Yixuan Zuo ◽  
...  
Keyword(s):  
Travel Time ◽  
Markov Models ◽  
Prediction Method ◽  
Time Difference ◽  
Location Prediction ◽  
Trajectory Data ◽  
Difference Model ◽  
The Difference ◽  
Real World Datasets ◽  
Joint Prediction

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

scholarly journals Dijkstra Algorithm for Determining the Shortest Path in the Case of Seven Hotels in Manado City Towards Manado’s Sam Ratulangi Airport.

d'CARTESIAN ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 158
Author(s):  
Yohana Permata Hutapea ◽  
Chriestie E.J.C. Montolalu ◽  
Hanny A.H. Komalig
Keyword(s):  
Graph Theory ◽  
Travel Time ◽  
Shortest Path ◽  
Travel Distance ◽  
Dijkstra’S Algorithm ◽  
Dijkstra Algorithm ◽  
Google Maps ◽  
Transportation Mode ◽  
Dijkstra's Algorithm ◽  
The Difference

Manado city has many notable tourist sites, resulting in the increase of the number of tourists visiting every year. Tourists require hotels with adequate facilities for their stay, such as 4-star hotels. After visiting Manado, tourists go back to where they come from. One of the transportation mode being used is airplanes. They then need a path to go through and not the usual one; they need the shortest path to get to Sam Ratulangi airport. Based on previous research, the shortest path is modeled by Graph Theory. Hotels will be represented as vertices, and the path from each hotels and to the airport will be represented as edges. The shortest path are searched by using Dijkstra’s Algorithm then will see the difference to shortest path from google maps. Based on the analysis results, Dijkstra’s Algorithm selects the shortest path with the smallest weight. The difference between Dijkstra’s Algorithm and google maps can be concluded that, in determining the shortest path used for the trip from the 4-star hotel to the airport, Dijkstra’s Algorithm is emphasized towards short travel distance, whereas google maps is emphasized more in short travel time.

Comparison of Inpatient Standard-of-Care to Outpatient Oritavancin Therapy for Patients With Acute Uncomplicated Cellulitis

2021 ◽  
pp. 089719002110212
Author(s):  
Brandy Williams ◽  
Justin Muklewicz ◽  
Taylor D. Steuber ◽  
April Williams ◽  
Jonathan Edwards
Keyword(s):  
Hospital Readmission ◽  
Average Length ◽  
Hospital Readmissions ◽  
Standard Of Care ◽  
Economic Outcomes ◽  
Day Hospital ◽  
Outpatient Parenteral Antimicrobial Therapy ◽  
Average Length Of Stay ◽  
Observational Cohort ◽  
The Difference

Background: Shifting inpatient antibiotic treatment to outpatient parenteral antimicrobial therapy may minimize treatment for acute bacterial skin and skin structure infections, including cellulitis. The purpose of this evaluation was to compare 30-day hospital readmission or admission due to cellulitis and economic outcomes of inpatient standard-of-care (SoC) management of acute uncomplicated cellulitis to outpatient oritavancin therapy. Methods: This retrospective, observational cohort study was conducted at a 941-bed community teaching hospital. Adult patients 18 years and older treated for acute uncomplicated cellulitis between February 2015 to December 2018 were eligible for inclusion. Information was obtained from hospital and billing department records. Patients were assigned to either inpatient SoC or outpatient oritavancin cohorts for comparison. Results: 1,549 patients were included in the study (1,348 in the inpatient SoC cohort and 201 in the outpatient oritavancin cohort). The average length of stay for patients admitted was 3.6 ± 1.5 days. The primary outcome of 30-day hospital readmission or admission due to cellulitis occurred in 49/1348 (3.6%) patients in the inpatient SoC cohort versus 1/201 (0.5%) in the outpatient oritavancin cohort (p = 0.02). The difference between costs and reimbursement was improved in the outpatient oritavancin group (p < 0.001). Conclusion: Outpatient oritavancin for acute uncomplicated cellulitis was associated with reduction in 30-day hospital readmissions or admissions compared to inpatient SoC. Beneficial economic outcomes for the outpatient oritavancin cohort were observed. Additional studies are required to confirm these findings.

scholarly journals “In cities, it’s not far, but it takes long”: comparing estimated and replicated travel times to reach life-saving obstetric care in Lagos, Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. e004318
Author(s):  
Aduragbemi Banke-Thomas ◽  
Kerry L M Wong ◽  
Francis Ifeanyi Ayomoh ◽  
Rokibat Olabisi Giwa-Ayedun ◽  
Lenka Benova
Keyword(s):  
Travel Time ◽  
Friction Surface ◽  
Obstetric Care ◽  
Travel Times ◽  
Night Time ◽  
Google Maps ◽  
Surface Approach ◽  
Low Resource ◽  
Life Saving ◽  
The Cost

BackgroundTravel time to comprehensive emergency obstetric care (CEmOC) facilities in low-resource settings is commonly estimated using modelling approaches. Our objective was to derive and compare estimates of travel time to reach CEmOC in an African megacity using models and web-based platforms against actual replication of travel.MethodsWe extracted data from patient files of all 732 pregnant women who presented in emergency in the four publicly owned tertiary CEmOC facilities in Lagos, Nigeria, between August 2018 and August 2019. For a systematically selected subsample of 385, we estimated travel time from their homes to the facility using the cost-friction surface approach, Open Source Routing Machine (OSRM) and Google Maps, and compared them to travel time by two independent drivers replicating women’s journeys. We estimated the percentage of women who reached the facilities within 60 and 120 min.ResultsThe median travel time for 385 women from the cost-friction surface approach, OSRM and Google Maps was 5, 11 and 40 min, respectively. The median actual drive time was 50–52 min. The mean errors were >45 min for the cost-friction surface approach and OSRM, and 14 min for Google Maps. The smallest differences between replicated and estimated travel times were seen for night-time journeys at weekends; largest errors were found for night-time journeys at weekdays and journeys above 120 min. Modelled estimates indicated that all participants were within 60 min of the destination CEmOC facility, yet journey replication showed that only 57% were, and 92% were within 120 min.ConclusionsExisting modelling methods underestimate actual travel time in low-resource megacities. Significant gaps in geographical access to life-saving health services like CEmOC must be urgently addressed, including in urban areas. Leveraging tools that generate ‘closer-to-reality’ estimates will be vital for service planning if universal health coverage targets are to be realised by 2030.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

Estimating Link Travel Time with Sparse GPS Data on Highway Corridors

2015 ◽  
Vol 2477 (1) ◽  
pp. 7-17 ◽  
Author(s):  
Qi Gong ◽  
Teresa M. Adams ◽  
Xiubin Bruce Wang
Keyword(s):  
Travel Time ◽  
Gps Data ◽  
Link Travel Time

Firm Characteristics, Relative Efficiency, and Equity Returns

2009 ◽  
Vol 44 (1) ◽  
pp. 213-236 ◽  
Author(s):  
Giao X. Nguyen ◽  
Peggy E. Swanson
Keyword(s):  
Explanatory Power ◽  
Stochastic Frontier ◽  
Efficiency Score ◽  
Equity Returns ◽  
Firm Characteristics ◽  
Stochastic Frontier Approach ◽  
Cross Sectional ◽  
Firm Efficiency ◽  
The Difference ◽  
Sectional Analysis

AbstractThis study uses a stochastic frontier approach to evaluate firm efficiency. The resulting efficiency score, based on firm characteristics, is the input for performance evaluation. The portfolio composed of highly efficient firms significantly underperforms the portfolio composed of inefficient firms even after adjustment for firm characteristics and risk factors, suggesting a required premium for the inefficient firms. The difference in performance between the two portfolios remains for at least five years after the portfolio formation year. In addition, firm efficiency exhibits significant explanatory power for average equity returns in cross-sectional analysis.

scholarly journals Characteristics Analysis of Freight Mode Choice Model According to the Introduction of a New Freight Transport System

2019 ◽  
Vol 11 (4) ◽  
pp. 1209 ◽  
Author(s):  
Seungjin Shin ◽  
Hong-Seung Roh ◽  
Sung Hur
Keyword(s):  
Travel Time ◽  
Transport System ◽  
Choice Model ◽  
Stated Preference ◽  
Statistical Significance ◽  
Explanatory Power ◽  
Service Level ◽  
Freight Transport ◽  
Transport Cost ◽  
Transport Mode

The purpose of this study is to identify the characteristics of freight mode choices made by shippers and carriers with the introduction of a new freight transport system. We set an area in which actual freight transport takes place as the analysis scope and performed a survey of the shippers and carriers that transport containers to identify their stated preference (SP) regarding the new freight mode. The SP survey was carried out through an experimental design and this study considered the three factors of transport time, transport cost, and service level. This study compared and analyzed the models by distance using an individual behavior model. The results of estimating the model showed that the explanatory power of the model classified by distance and the individual parameters have statistical significance. The hit ratio was also high, which confirms that the model was estimated properly. In addition, the range of elasticity and the value of travel time analyzed using the model were evaluated to be appropriate compared to previous studies. The findings of the elasticity analysis show that strategies for reducing the transport cost are effective to increase the demand for the new transport mode. The value of travel time of freight transport was found to be higher than the current value generally applied in Korea. Considering that the value of travel time currently used is based on road freight transport, further research is required to apply a new value of travel time that reflects the characteristics of the new transport mode in the future.

Influence of Blood Group and Secretor Status on Carbohydrate Structures in Human Gastric Mucins: Implications for Peptic Ulcer

1995 ◽  
Vol 89 (4) ◽  
pp. 405-415 ◽  
Author(s):  
R. L. Sidebotham ◽  
J. H. Baron ◽  
J. Schrager ◽  
J. Spencer ◽  
J. R. Clamp ◽  
...  
Keyword(s):  
Peptic Ulcer ◽  
Amino Acid ◽  
Blood Group ◽  
Average Length ◽  
Amino Acid Residues ◽  
Secretor Status ◽  
Increased Risk ◽  
The Mean ◽  
The Difference ◽  
Carbohydrate Chains

1. The content and distribution of carbohydrate was examined in mucus glycopolypeptides from human antral mucosae. 2. The mean amount of carbohydrate per 1000 amino acid residues was found to be similar in glycopolypeptides with A, B or H activity. It was slightly, though significantly, less in glycopolypeptides lacking these determinants, because carbohydrate chains were of a shorter average length than in the A-, B- or H-active preparations. This difference was reflected in the sizes of oligosaccharide—alcohols released from representative glycopolypeptides with alkaline borohydride. 3. Differences between A-, B- or H-active and non-secretor glycopolypeptides in terms of the mean number of carbohydrate chains per 1000 amino acid residues were found to be small, and without significance. 4. The average number of peripheral monosaccharide units per 1000 amino acid residues was greater in A-active than in H-active, and least in non-secretor, glycopolypeptides. This order was reversed for monosaccharide units incorporated into skeletal (core plus backbone) structures. The difference in each case was statistically significant. 5. These findings suggest that the increased risk of peptic ulcer associated with blood group O and non-secretor status is unlikely to be attributable to an inherent deficiency in the protective mucus layer, linked to differences between mucins that are associated with A, B or H activity. Other hypotheses linked to infection with Helicobacter pylori are examined.

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