Traffic congestion has become one of the major challenging problems of modern life in many urban areas. This growing problem leads to negative environmental impacts, wasted fuel, lost productivity, and increased travel time. In big cities, trains and buses bring riders to transit stations near shopping and employment centers, but riders then need another transportation mode to reach their final destination, which is known as the last mile problem. A smart bike-sharing system (BSS) can help address this problem and encourage more people to ride public transportation, thus relieving traffic congestion.
At the strategic level, we start with proposing a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms. In the transportation sector, researchers can use smartphones to track and obtain information of multi-mode trips. These data can be used to recognize the user's transportation mode, which can be then utilized in several different applications; such as planning new BSS instead of using costly surveys. Next, a new method is proposed to quantify the effect of several factors such as weather conditions on the prediction of bike counts at each station. The proposed approach is promising to quantify the effect of various features on BSSs in cases of large networks with big data. Third, these resulted significant features were used to develop state-of-the-art toolbox algorithms to operate BSSs efficiently at two levels: network and station. Finally, we proposed a quality-of-service (QoS) measurement, namely Optimal Occupancy, which considers the impact of inhomogeneity in a BSS. We used one of toolbox algorithms modeled earlier to estimate the proposed QoS. Results revealed that the Optimal Occupancy is beneficial and outperforms the traditionally-known QoS measurement. / PHD / A growing population, with more people living in cities, has led to increased pollution, noise, congestion, and greenhouse gas emissions. One possible approach to mitigating these problems is encouraging the use of bike-sharing systems (BSSs). BSSs are an integral part of urban mobility in many cities and are sustainable and environmentally friendly. As urban density increases, it is likely that more BSSs will appear due to their relatively low capital and operational costs, ease of installation, pedal assistance for people who are physically unable to pedal for long distances or on difficult terrain, and the ability to track bikes in some cases.
This dissertation is a building block for a smart BSS in the strategic level, which could be used in real and different applications. The main aims of the dissertation are to boost the redistribution operation, to gain new insights into and correlations between bike demand and other factors, and to support policy makers and operators in making good decisions regarding planning new or existing BSS.
This dissertation makes many significant contributions. These contributions include novel methods, measurements, and applications using machine learning and statistical learning techniques in order to design a smart BSS. We start with proposing a novel framework that increases the accuracy of traditional transportation mode classification algorithms. In the transportation sector, researchers can use smartphones to track and obtain information of multi-mode trips. These data can be used to recognize the user’s transportation mode, which can be then used in planning new BSS. Next, a new method is proposed to quantify the effect of several factors such as weather conditions on the prediction of bike station counts. Third, we use state-of-the-art data analytics to develop a toolbox to operate BSSs efficiently at two levels: network and station. Finally, we propose a quality-of-service (QoS) measurement, which considers the impact of inhomogeneity of BSS properties.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/97827 |
| Date | 25 October 2018 |
| Creators | Ashqar, Huthaifa Issam |
| Contributors | Civil and Environmental Engineering, Rakha, Hesham A., House, Leanna L., Abbas, Montasir M., Yang, Hao |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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