Reliable shortest path finding in stochastic time-dependent road network with spatial-temporal link correlations: A case study from Beijing

Chen, Peng; Tong, Rui; Yu, Bin; Wang, Yunpeng

doi:10.1016/j.eswa.2020.113192

Cited by 28 publications

(4 citation statements)

References 51 publications

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“…Based on the experimental results, they recommend the Pape ( 49 ) algorithm and TQ algorithm ( 50 ) that are efficient at solving the one-to-all shortest-path problem. With the advent of the variation of the SP problem, other efficient algorithms have been proposed to solve it ( 51 – 53 ). Accordingly, we modified the label-setting algorithm to solve the transportation mode recommendation in the context of urban trip design.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization for the Multiday Urban Personalized Trip Design Problem With Time Windows and Transportation Mode Recommendations

Jian

Wang

2023

Transportation Research Record: Journal of the Transportation R

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The urban tourism market has been increasingly dominated by the demand for personalized experiences. This paper focuses on the multiday urban personalized trip design problem considering the time windows and transportation mode recommendations (MUPTDP-TW-TMR). A two-stage methodology is proposed to solve this problem. In the first stage, a linear programming model for the multiday urban personalized trip design problem with time windows (MUPTDP-TW) is proposed. A modified memetic algorithm (MMA) is devised to solve it, containing efficient operators. In the second stage, a novel transportation mode recommendation model is developed, which considers the diversity of tourist preferences. A modified label-setting algorithm is applied to solve the model. The data from Chongqing, the most popular city in Southwest China, are collected to evaluate the performance of our approach. The case study based on tourist and popular preferences is conducted. Compared with the state-of-the-art algorithms, the experimental results show that our approach can design more reasonable and personalized itineraries for tourists.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization for the Multiday Urban Personalized Trip Design Problem With Time Windows and Transportation Mode Recommendations

Jian

Wang

2023

Transportation Research Record: Journal of the Transportation R

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“…This approach provides an analytical solution for estimating the parameters of a lognormal distribution as the fit for the distribution of a sum of generally correlated random variables. This approach is utilized in a few studies considering STV networks, and, here, the implementation presented by Chen et al is followed (29).…”

Section: Other Distribution Estimation Approaches In the Literaturementioning

confidence: 99%

“…The segment search approach was performed using a minimum-number-ofobservations threshold at 30 and it was tested by using the resulting segments to estimate the path distributions using the MCS approach with independent random variables and assuming time-dependence only, which will be abbreviated as MCS-SS-I and MCS-SS-TD. The two approaches from the literature, the ensemble MCMC sampler with M-H walkers (abbreviated MCMC-MH), and the approach for approximating a sum of random variables via a lognormal (abbreviated LogN), were tested with the same data and for the same set of path travel time distributions (26)(27)(28)(29).…”

Section: Experiments Designmentioning

confidence: 99%

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

Filipovska

Mahmassani

Mittal

2021

Transportation Research Record

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Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

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“…Based on multi-level and hierarchical characteristics of road networks, scholars have also proposed a series of the shortest path accelerated search methods based on multi-level road networks [4][5][6][7][8][9]. In addition to employing distance as an optimal condition, numerous studies have been performed to determine the shortest time path in a traffic network with dynamically changing weights, developing effective shortest time path planning algorithms in time-varying road networks [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Cycling Trajectory-Based Navigation Independent of Road Network Data Support

Zhang

Zhao

Feng

et al. 2021

IJGI

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The popularization of smart phones and the large-scale application of location-based services (e.g., exercises, traveling and food delivery via cycling) have resulted in the emergence of massive amounts of personalized cycling trajectory data, spurring the demand for map navigation based on cycling trajectories. Therefore, in the current paper, we propose a cycling trajectory-based navigation algorithm without the need for road network data support. The proposed algorithm focuses on extracting navigation information from a given trajectory and then guiding others to the destination along the original trajectory. In particular, the algorithm analyzes the coordinate and azimuth angle data collected by the built-in positioning and direction sensors of mobile smart phones to identify several turning modes from the provider’s cycling trajectory. In addition, the interference of the traffic conditions during data collection is considered in order to improve the recognition accuracy of the turning modes. The turning modes in the trajectory are subsequently transformed into navigation information and shared with users, so as to realize the shared navigation of the cycling trajectory. Experimental results indicate that the algorithm can accurately extract the turning feature points from cycling trajectory data, recognize various turning modes and generate correct navigation messages, thereby guiding users to arrive at the destination safely and accurately along the original trajectory. The algorithm is independent of electronic map platforms and does not require road network data support.

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Reliable shortest path finding in stochastic time-dependent road network with spatial-temporal link correlations: A case study from Beijing

Cited by 28 publications

References 51 publications

Optimization for the Multiday Urban Personalized Trip Design Problem With Time Windows and Transportation Mode Recommendations

Optimization for the Multiday Urban Personalized Trip Design Problem With Time Windows and Transportation Mode Recommendations

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

Cycling Trajectory-Based Navigation Independent of Road Network Data Support

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