KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 10 of 19

Some of the metrics are blocked by your
consent settings
GPS Data Analytics for the Assessment of Public City Bus Transportation Service Quality in Bangkok
(2023-04-01)
Chawuthai, Rathachai
;
Sumalee, Agachai
;
Threepak, Thanunchai
Evaluation of the quality of service (QoS) of public city buses is generally performed using surveys that assess attributes such as accessibility, availability, comfort, convenience, reliabilities, safety, security, etc. Each survey attribute is assessed from the subjective viewpoint of the service users. This is reliable and straightforward because the consumer is the one who accesses the bus service. However, in addition to summarizing personal feedback from humans, using data analytics has become another useful method for assessing the QoS of bus transportation. This work aims to use global positioning system (GPS) data to measure the reliability, accessibility, and availability of bus transportation services. There are three QoS scoring functions for tracking complete trips, on-path driving, and on-schedule operation. In the analytical process, GPS coordinates rounding is adopted and applied for detecting trips on each route path. After assessing the three QoS scores, it has been found that most bus routes have good operations with high scores, while some bus routes show room for improvement. Future work could use our data to create recommendations for policy makers in terms of how to improve a city’s smart mobility.
Some of the metrics are blocked by your
consent settings
Travel Time Prediction on Long-Distance Road Segments in Thailand
(2022-06-01)
Chawuthai, Rathachai
;
Ainthong, Nachaphat
;
Intarawart, Surasee
;
Boonyanaet, Niracha
;
Sumalee, Agachai
This study proposes a method by which to predict the travel time of vehicles on long-distance road segments in Thailand. We adopted the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter to predict the travel time on each road segment using historical data from the Global Positioning System (GPS) tracking of trucks in Thailand. As a result, our prediction method gave a Mean Absolute Error (MAE) of 12.15 min per 100 km, whereas the MAE of the baseline was 27.12 min. As we can estimate the travel time of vehicles with a lower error, our method is an effective way to shape a data-driven smart city in terms of predictive mobility.
Some of the metrics are blocked by your
consent settings
Smarter and more connected: Future intelligent transportation system
(2018-07-01)
Sumalee, Agachai
;
Ho, Hung Wai
Emerging technologies toward a connected vehicle-infrastructure-pedestrian environment and big data have made it easier and cheaper to collect, store, analyze, use, and disseminate multi-source data. The connected environment also introduces new approaches to flexible control and management of transportation systems in real time to improve overall system performance. Given the benefits of a connected environment, it is crucial that we understand how the current intelligent transportation system could be adapted to the connected environment.
Some of the metrics are blocked by your
consent settings
Network-wide on-line travel time estimation with inconsistent data from multiple sensor systems under network uncertainty
(2018-01-02)
Shao, Hu
;
Lam, William H.K.
;
Sumalee, Agachai
;
Chen, Anthony
This paper proposes a new modeling approach for network-wide on-line travel time estimation with inconsistent data from multiple sensor systems. It makes full use of both the available data from multiple sensor systems (on-line data) and historical data (off-line data). The first- and second-order statistical properties of the on-line data are investigated together with the data inconsistency issue to estimate network-wide travel times. The proposed model is formulated as a generalized least squares problem with non-linear constraints. A solution algorithm based on the penalty function method is adopted to solve the proposed model, whose application is illustrated by numerical examples using a local road network in Hong Kong.
Some of the metrics are blocked by your
consent settings
A real-time bus arrival time information system using crowdsourced smartphone data: a novel framework and simulation experiments
(2018-01-02)
Wepulanon, Piyanit
;
Sumalee, Agachai
;
Lam, William H.K.
This paper proposes a novel framework for developing a real-time bus arrival time information system, using crowdsourced bus information contributed by bus passengers. On the one hand, passengers can derive the real-time information via their smartphones. On the other hand, they can provide some bus data in return. Particular characteristics of the participatory-based bus data are introduced. Also, a number of data processing steps are proposed in the framework to handle the data characteristics, which pose extra difficulties in real-time bus arrival time prediction. The proposed system is evaluated using simulated bus data sets. Practicality of the system is investigated in terms of prediction accuracy based on different participation percentages of bus passengers.
Some of the metrics are blocked by your
consent settings
Freight Traffic Analytics from National Truck GPS Data in Thailand
(2018-01-01)
Chankaew, Nattaphon
;
Sumalee, Agachai
;
Treerapot, Siripirote
;
Threepak, Thanunchai
;
Ho, H. W.
Recently, demand of freight transport increases tremendously and cause serious impacts on traffic congestion and air quality. In 2015, The Department of Land Transport of Thailand has introduced a project named "Nationwide Confidence with GPS Onboard" to install GPS tracking system on all trucks in Thailand. This project provides new sources of data in analyzing the freight-related traffics and designing remedial measures for freight-related issues. This paper aims to demonstrate the use of GPS data in determining truck activities, estimating truck origin-destination matrix and estimating the flow of different commodities. Thailand is used as a case study to demonstrate the results.
Some of the metrics are blocked by your
consent settings
Estimation and analysis of Thai national freight demands from GPS data
(2018-01-01)
Siripirote, Treerapot
;
Sumalee, Agachai
;
Ho, H. W.
;
Chankaew, Nattaphon
;
Jedwanna, Krit
Freight transport has been increased in both demand and distance travelled in recent years due to the changes in consumer purchasing patterns and the improvements in the transportation systems. Such increases cause serious adverse impacts on traffic conditions and air qualities. This paper proposes a framework that make use of passively collected truck Global Positioning System (GPS) data and link counts in the estimation of national freight demands, which are the crucial quantities in the design of management and remedial measures. Effective truck stops and the corresponding activities will be determined from the GPS data and used in estimating the observed freight demands. Such observed demands will be used with the link counts to determine the national freight demands. An empirical example from Thailand is adopted in this paper to illustrate the proposed framework in estimating the national freight demands from GPS data and link counts.
Some of the metrics are blocked by your
consent settings
The use of natural rubber latex as a renewable and sustainable modifier of asphalt binder
(2017-06-03)
Wen, Yong
;
Wang, Yuhong
;
Zhao, Kecheng
;
Sumalee, Agachai
Natural rubber (NR) powder as a bio-modifier of asphalt binder has been shown to have some beneficial effects. However, there is limited research into the use of the liquid form of NR, i.e. concentrated NR latex, as an asphalt binder modifier. Compared to NR powder, NR latex is cheaper and more accessible in some countries, and potentially creates viscosity-reducing foams in the modified binder during mixture production. In this research, asphalt binders modified with different amount of NR latex were systematically studied, including the rotational viscosities, rutting resistance, fatigue resistance, low-temperature behaviour and temperature sensitivity. The dispersion of the NR latex in the modified binders was examined using fluorescence microscope and atomic force microscope. Test results indicate that the addition of NR latex increases the viscosity and elastic recovery of the modified binders and potentially enhances asphalt pavements’ resistance to rutting, thermal cracking and fatigue damage. The NR latex also reduces the temperature sensitivity of the modified binders. The optimum NR latex content was found to be 7% of the total mass of the modified binder. A network of extensive microstructures mixed with bubbles was identified in the modified binders under heat. As a renewable and sustainable material, NR latex has the potential to be used as an effective asphalt modifier.
Some of the metrics are blocked by your
consent settings
Taxi demand analytics and driver's recommendation system: Algorithm and evaluation
(2017-01-01)
Sumalee, Agachai
;
Buthong, Sakon
Finding more passengers is the most important problem for newbie taxi drivers. Even experienced drivers who are not familiar with particular areas. They have to find more passengers as soon as possible because each cruise takes more energy cost and creates opportunity loss. This research introduced the integration of a recommendation system for taxi drivers, which can guarantee the best profits them. To clarify how the system works, after dropping off passengers, three recommendations are displayed to the drivers, i.e., hubs, POIs and routes as digital maps on their mobile devices. Each recommendation relates to the demand for taxis in that area and depends on the time-of-day analyzed by data mining algorithms. We implemented the developed system and showed the performance of the real-world taxi system supported by All Thai Taxi. The drivers' performances were improved by up to 10.5% after the use of the recommendation system.
Some of the metrics are blocked by your
consent settings
Estimation of mean and covariance of stochastic multi-class OD demands from classified traffic counts
(2015-10-01)
Shao, Hu
;
Lam, William H.K.
;
Sumalee, Agachai
;
Hazelton, Martin L.
This paper proposes a new model to estimate the mean and covariance of stochastic multi-class (multiple vehicle classes) origin-destination (OD) demands from hourly classified traffic counts throughout the whole year. It is usually assumed in the conventional OD demand estimation models that the OD demand by vehicle class is deterministic. Little attention is given on the estimation of the statistical properties of stochastic OD demands as well as their covariance between different vehicle classes. Also, the interactions between different vehicle classes in OD demand are ignored such as the change of modes between private car and taxi during a particular hourly period over the year. To fill these two gaps, the mean and covariance matrix of stochastic multi-class OD demands for the same hourly period over the year are simultaneously estimated by a modified lasso (least absolute shrinkage and selection operator) method. The estimated covariance matrix of stochastic multi-class OD demands can be used to capture the statistical dependency of traffic demands between different vehicle classes. In this paper, the proposed model is formulated as a non-linear constrained optimization problem. An exterior penalty algorithm is adapted to solve the proposed model. Numerical examples are presented to illustrate the applications of the proposed model together with some insightful findings on the importance of covariance of OD demand between difference vehicle classes.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results