On demand forecasting of demand-responsive paratransit services with prior reservations

Chandakas, Ektoras

doi:10.1016/j.trc.2020.102817

Cited by 13 publications

(8 citation statements)

References 28 publications

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“…At the same time, the accuracy of the model can be expected to be on par with non-parametric models. Chandakas (2020) suggests that, in modeling in linear relationships between variables, econometric models and deep learning models do not have significant differences in their performance. This is especially pertinent for our case since increases in the number of SARS‑CoV‑2 cases and in the stringency of measures can lead linearly to decreases in ridership, as it can be proved by the statistical significance and the intuitive values of the exogenous variables in the ARIMAX model.…”

Section: Discussionmentioning

confidence: 99%

“…Short- and medium-term demand models on the other hand mainly involve discretizing time into fixed intervals and predicting demand using observations from past intervals. In transportation modeling, they are frequently applied in road traffic applications (mainly in order to access flows and travel-times), and in similar public transit applications ( Chandakas, 2020 ). They are especially relevant for bus lines and networks, who typically exhibit mode degrees of freedom in relation to train networks, being necessary for effective bus operations management ( Ceder et al, 2013 ).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece

Giouroukelis

Papagianni²,

Tzivellou³

et al. 2022

Case Studies on Transport Policy

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece

Giouroukelis

Papagianni²,

Tzivellou³

et al. 2022

Case Studies on Transport Policy

View full text Add to dashboard Cite

“…Because demand prediction must precede the efficient operation of DRT services, many studies have recently been conducted using various methodologies. For example, in [8], after the entire region was divided into grids, the demand for a DRT service was predicted using a convolutional neural network (CNN), LSTM, and ConvLSTM, along with exogenous variables such as weather. In [9], an appropriate DRT type was identified by estimating the average number of people getting on and off at bus stops in a regular pattern identified through cluster classification of time-by-time boarding points for the efficient placement of DRT.…”

Section: Related Work and Preliminariesmentioning

confidence: 99%

“…The demand forecasts for DRT services and taxis are highly similar [8]. Therefore, to predict the general taxi demand, the entire area is converted into an image set to a grid of a specific size and utilized.…”

Section: Grid-based Inflow and Outflow Predictionmentioning

confidence: 99%

PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport

Lee

Choi

Kim

2023

Journal of Advanced Transportation

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To provide an efficient demand-responsive transport (DRT) service, we established a model for predicting regional movement demand that reflects spatiotemporal characteristics. DRT facilitates the movement of restricted passengers. However, passengers with restrictions are highly dependent on transportation services, and there are large fluctuations in travel demand based on the region, time, and intermittent demand constraints. Without regional demand predictions, the gaps between the desired boarding times of passengers and the actual boarding times are significantly increased, resulting in inefficient transportation services with minimal movement and maximum costs. Therefore, it is necessary to establish a regional demand generation prediction model that reflects temporal features for efficient demand response service operations. In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. The proposed model considers a region’s unique characteristics and the influence between regions through spatial information, such as the proximity between regions, convenience of transportation, and functional similarity. In addition, three types of temporal characteristics—adjacent visual characteristics, periodic characteristics, and representative characteristics—were defined to reflect past demand patterns. With the proposed demand forecasting model, measures can be taken, such as having empty vehicles move to areas where demand is expected or encouraging adjustment of the vehicle’s rest time to avoid congestion. Thus, fast and efficient transportation satisfying the movement demand of passengers with restrictions can be achieved, resulting in sustainable transportation.

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“…The reservation model is widely used in real-life scheduling problems [27][28][29]. The solution steps of this model are carried out in the following order: the data collection must be completed first and then perform data pre-processing, bring in the optimization model to solve, and, finally, visualize the results.…”

Section: Single Elevator Booking Modelmentioning

confidence: 99%

A Real-Time Matrix Iterative Optimization Algorithm of Booking Elevator Group and Numerical Simulation Formed by Multi-Sensor Combination

Chen

Zheng

Liu³

et al. 2021

Electronics

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Elevators are an essential indoor transportation tool in high-rise buildings. The world is advocating the design concept of safety, energy-saving, and intelligence. We focus on improving operation speed and utilization efficiency of the elevator group. This paper proposed a real-time reservation elevator groups optimization algorithm, and a dynamic matrix iterative model has been established. The indoor navigation technology UWB is applied, which can help users to quickly find elevators. The manned equilibrium efficiency and running time equilibrium efficiency of elevator group are given. Moreover, the data filtering criterion formulas for user waiting time and elevator remaining space are defined. In this paper, three numerical examples are given. Example 1 is a single elevator in n-storey building. Example 2 is compared with different scheduling algorithms, such as FCFS, SSTF, LOOK, and SCAN algorithms, and the results show that our method has the advantages of short total running time and less round-trip frequency. At last, the matrix of numerical iteration results are visualized, and the data movement status of people on each floor can be observed. Example 3 introduced elevator group algorithms. For high-rise buildings, this paper adopts a high, medium, and low hierarchical management model; this model has high coordination, as well as fast response, batch process, and adaptive function. Finally, we also discussed and compared the complexity of single elevator and elevator group algorithms. Therefore, this method has great development potential and practical application value, which deserves further study.

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On demand forecasting of demand-responsive paratransit services with prior reservations

Cited by 13 publications

References 28 publications

Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece

Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece

PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport

A Real-Time Matrix Iterative Optimization Algorithm of Booking Elevator Group and Numerical Simulation Formed by Multi-Sensor Combination

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