Novel online routing algorithms for smart people‐parcel taxi sharing services

Abstract

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.

Determinants of apartment price volatility in Vietnam: a comparison between Hanoi and Ho Chi Minh City

“…In the experiments, we have chosen some well-known NHPPs learning/estimation problems from the literature that model different kinds of events such as daily taxi call arrivals (e.g., Son et al, 2022;Yoon et al, 2019;Liu et al, 2019) and telephone call arrivals at customer service call centers (e.g., Brown et al, 2005;Shen and Huang, 2008;Xu et al, 2017). For these problems, the true rate functions are unknown and dP ← dividedInterval(0, T, O, dP )…”

“…Table 4 shows the profit of scheduling algorithms. We denote the OTSF-DP algorithm in Son et al (2022) by p 1 and the OTSF-DP using our new predicted rate function by p 2 . The percentage of improvement is denoted by ρ = (p 2 − p 1 )100/p 1 .…”

“…One of the most fundamental and important of such processes is the Poisson process 1 arXiv:2402.12808v1 [cs.LG] 20 Feb 2024 (Komaki, 2021;Jahani et al, 2021;Moore and Davenport, 2019;Alizadeh and Papp, 2013) as it is appropriate for modelling a large number of completely random phenomena. Therefore, the Poisson process has been widely used in various real-world applications such as taxi routing (Son et al, 2022), queueing systems (Brown et al, 2005), and e-mail arrival counting (Alizadeh et al, 2008;Alizadeh and Papp, 2013).…”

“…A large geographical area can be clustered into non overlap sub-areas by the K-means clustering algorithm. Most of recent works on DVRP have utilized this approach, for instance,Ferrucci (2013);Son et al (2022). InSon et al (2022), the authors consider the share-a-ride taxi scheduling problem and assume that the arrival rate of taxi requests only depend on period and area and hence are fixed overall days.…”

Special issue on smart cities and its applications

“…The share‐a‐ride problem has been spotlighted as an efficient planning model to facilitate the flow of people and goods in smart cities. In this regard, the fourth paper, [9] “Novel Online Routing Algorithms for Smart People–Parcel Taxi Sharing Services” by Nguyen and others, proposed a new people–parcel taxi sharing model that can accommodate more parcel delivery requests, in addition to rider requests, while minimizing taxi idle time and distance. Unlike conventional approaches, the proposed algorithms apply learned information based on historical data analysis of routing with the objectives of maximizing overall travel efficiency and minimizing driver idle time.…”