scholarly journals Competition measures of TOD point to Central Business Districts in Surabaya using travel time approach (a case study: Joyoboyo Terminal)

2020 ◽  
Vol 930 ◽  
pp. 012066
Author(s):  
A A Zahra ◽  
E Ahyudanari
Keyword(s):  
Travel Time ◽  
Central Business Districts

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

2021 ◽  
pp. 036119812110315
Author(s):  
Markus Steinmaßl ◽  
Stefan Kranzinger ◽  
Karl Rehrl
Keyword(s):  
Travel Time ◽  
Time Of Day ◽  
Temporal Aggregation ◽  
Travel Time Reliability ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Depth Analysis ◽  
Data Source ◽  
Travel Time Index

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

MEASURING SPATIAL HEALTHCARE FACILITIES ACCESSIBILITY USING TWO-STEP FLOATING CATCHMENT ANALYSIS (2SFCA) (Case Study: Cianjur Regency, Indonesia)

2021 ◽  
pp. 11
Author(s):  
Muhamad Iqbal Januadi Putra ◽  
Nabila Dety Novia Utami
Keyword(s):  
Travel Time ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Spatial Accessibility ◽  
Healthcare Facilities ◽  
Spatial Disparities ◽  
Time Threshold

The presence of healthcare facilities is quite essential to provide good healthcare services in a particular area, however, the existence of healthcare facilities is not evenly distributed in Cianjur Regency. This condition leads to the disparities of healthcare facilities across the Cianjur Regency. In this paper, we aim to measure and map the spatial disparities of healthcare facilities using a Two-Step Floating Catchment Analysis (2SFCA). This method can calculate the magnitude of spatial accessibility for healthcare facilities by formulating the travel time threshold and the quality of healthcare facilities across the study area. This research shows the result that the spatial accessibility of healthcare facilities in the Cianjur Regency is not evenly distributed across the districts. The spatial accessibility value resulted from 2SFCA is ranging from 0- 3.97. A low value indicates low spatial accessibility, while a higher value shows good accessibility. The majority of districts in the Cianjur Regency have the spatial accessibility value 0-0.5 (86%). Meanwhile, only a few have the higher value; value 0.5-0.99 as much as 6.6%, 0.99-1.49 as 3.3%, and 3.48-3.97 has a percentage of 3.3%. Also, this analysis results in the cluster of good spatial accessibility in healthcare facilities, namely the Pagelaran District and Cipanas District. Interestingly, the downtown of Cianjur Regency has lower spatial accessibility compared to both areas.

A case study: travel time inversion for P-wave velocity using OBS data of South China Sea

2012 ◽  
Vol 33 (4) ◽  
pp. 389-396 ◽  
Author(s):  
Xiangchun Wang ◽  
Timothy A. Minshull ◽  
Changliang Xia ◽  
Xuewei Liu
Keyword(s):  
South China Sea ◽  
Travel Time ◽  
Wave Velocity ◽  
South China ◽  
P Wave ◽  
Time Inversion ◽  
P Wave Velocity ◽  
Travel Time Inversion ◽  
Obs Data

Freight Vehicle Travel Time Analysis: A Case Study of Birgunj Nagdhunga Corridor

2021 ◽  
Vol 08 (02) ◽  
pp. 1-13
Author(s):  
Abhishek Jha ◽  
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
License Plate ◽  
Monitoring Method ◽  
Factors Affecting ◽  
Average Travel Time ◽  
Major Factors ◽  
Vehicle Travel Time ◽  
Freight Vehicles

This study covers the freight vehicle, which clears the custom clearance process for Kathmandu and transports the same goods to Kathmandu from Birgunj. In this study average travel time for freight vehicles from Birgunj to Nagdhunga has been studied, along with the factors affecting the travel time from Birgunj to Nagdhunga. License plate monitoring method of the freight vehicles was done to find the average travel time and a questionnaire survey was done to identify the factors affecting travel time of the freight vehicle. The travel time from Birgunj to Nagdhunga is different for different types of, vehicle and good. The fastest average travel time is of fixed container of 40 feet size with 23.2 hours and longest average time is for fixed container of 20 feet size with 28.95 hours. The average travel time for non-degradable goods is 26.5 hours and for degradable goods is 22.38 hours. Major factors affecting the travel time are traffic congestion along the route, bad road condition along the route and hilly road with sharp bends, turns and grade.

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