• ETRI Journal
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• 제44권4호
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• pp.624-640
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• 2022

Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.

• ETRI Journal
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• 제44권2호
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• pp.220-231
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• 2022

Building smart transportation services in urban cities has become a worldwide problem owing to the rapidly increasing global population and the development of Internet-of-Things applications. Traffic congestion and environmental concerns can be alleviated by sharing mobility, which reduces the number of vehicles on the road network. The taxi-parcel sharing problem has been considered as an efficient planning model for people and goods flows. In this paper, we enhance the functionality of a current people-parcel taxi sharing model. The adapted model analyzes the historical request data and predicts the current service demands. We then propose two novel online routing algorithms that construct optimal routes in real-time. The objectives are to maximize (as far as possible) both the parcel delivery requests and ride requests while minimizing the idle time and travel distance of the taxis. The proposed online routing algorithms are evaluated on instances adapted from real Cabspotting datasets. After implementing our routing algorithms, the total idle travel distance per day was 9.64% to 12.76% lower than that of the existing taxi-parcel sharing method. Our online routing algorithms can be incorporated into an efficient smart shared taxi system.

• 대한교통학회지
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• 제31권5호
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• pp.79-88
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• 2013

'교통약자의 이동편의 증진법'이 제정됨에 따라, 각 지자체에서는 교통약자의 이동권을 보장하기 위해 지하철 역사 엘리베이터 설치, 저상버스 공급, 특별교통수단 도입 등의 다양한 노력을 해오고 있다. 그러나, 특별교통수단의 경우 예산의 제약으로 인하여 늘어나는 장애인 교통수요에 대비한 차량의 증차가 미흡한 상태이다. 따라서 본 연구에서는 부산광역시 특별교통수단 '두리발'을 대상으로 특별교통수단 이용실태 및 장애인의 교통수단 선택특성을 고찰하였다. 또한, 일반택시의 요금할인을 통하여 특별교통수단을 이용하는 장애인의 교통수요 분산을 유도하고자, 순서형 로지스틱 모형을 이용하여 요금할인에 따른 일반택시 이용 예측모형을 구축하였다. 본 연구의 결과는 특별교통 수단을 이용하는 교통약자의 이동효율 증진을 촉진할 수 있는 정책의 기초 자료로 사용될 것으로 기대한다.