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Smart Cities
Special Issues
Cost-Effective Transportation Planning for Smart Cities
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Cost-Effective Transportation Planning for Smart Cities

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Smart Transportation".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 4895

Special Issue Editors

Dr. Cristian Poliziani

E-Mail Website
Guest Editor
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Interests: transport systems; traffic simulation; pollutant emissions; energy use; transport planning; optimization problems
Dr. Haitam Laarabi

E-Mail Website
Guest Editor
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Interests: transport systems; traffic simulation; pollutant emissions; energy use; transport planning; optimization problems; ride-hailing; pooling; on-demand mobility

Special Issue Information

Dear Colleagues,

Cost–benefit analyses of transport policies, services, and technologies can aid in identifying the most efficient strategies for smart cities, enhancing user experience, and reducing environmental impacts.

An efficient transport strategy should be able to minimize implementation costs, while, at the same time, minimizing person- and environment-related negative impacts, such as those linked to accessibility, equity, energy use, and pollutant emissions.

We look forward to reviewing contributions focusing on, but not limited to, the following topics: electric fleet charging management, ride-hailing fleet repositioning, carpooling, transit enhancement, transport demand management, incentives to use more active modes of transport, and first- and last-mile connections.

We welcome research on optimization, artificial intelligence, machine learning, and simulation models that can help in identifying more efficient transport alternatives.

Dr. Cristian Poliziani
Dr. Haitam Laarabi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Smart Cities is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart cities
  • transport system
  • planning
  • cost analysis
  • accessibility
  • equity
  • environment
  • energy
  • simulation
  • optimization
  • machine learning
  • artificial intelligence

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Published Papers (5 papers)

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Research

35 pages, 3860 KiB  
Article
A Cross-Sectional Study on the Public Perception of Autonomous Demand-Responsive Transits (ADRTs) in Rural Towns: Insights from South-East Queensland
by Shenura Jayatilleke, Ashish Bhaskar and Jonathan M. Bunker
Smart Cities 2025, 8(3), 72; https://doi.org/10.3390/smartcities8030072 - 23 Apr 2025
Viewed by 253
Abstract
Rural public transport networks face significant challenges, often characterised by suboptimal service quality. With advancements in technology, various applications have been explored to address these issues. Autonomous Demand-Responsive Transits (ADRTs) represent a promising solution that has been investigated over recent years. Their potential [...] Read more.
Rural public transport networks face significant challenges, often characterised by suboptimal service quality. With advancements in technology, various applications have been explored to address these issues. Autonomous Demand-Responsive Transits (ADRTs) represent a promising solution that has been investigated over recent years. Their potential to enhance the overall quality of transport systems and promote sustainable transportation is well-recognised. In our research study, we evaluated the viability of ADRTs for rural networks. Our methodology focused on two primary areas: the suitability of ADRTs (considering vehicle type, service offerings, trip purposes, demographic groups, and land use) and the broader impacts of ADRTs (including passenger performance, social impacts, and environmental impacts). Perceptions of ADRT suitability peaked for university precincts and 24/7 operations. However, they were less favoured by mobility-disadvantaged groups (disabled, seniors, and school children). We also examined demographic heterogeneity and assessed the influence of demographic factors (age, gender, education, occupation, household income level, and disability status) on the implementation of ADRTs in rural settings. The findings delineate the varied perceptions across these socio-demographic strata, underscoring the necessity for demographic-specific trials. Consequently, we advocate for the implementation of ADRT services tailored to accommodate the diverse needs of these demographic cohorts. Full article
(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)
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19 pages, 3902 KiB  
Article
DRBO—A Regional Scale Simulator Calibration Framework Based on Day-to-Day Dynamic Routing and Bayesian Optimization
by Xuan Jiang, Yibo Zhao, Chonghe Jiang, Junzhe Cao, Alexander Skabardonis, Alex Kurzhanskiy and Raja Sengupta
Smart Cities 2025, 8(2), 49; https://doi.org/10.3390/smartcities8020049 - 13 Mar 2025
Viewed by 483
Abstract
Traffic simulation, a tool for recreating real-life traffic scenarios, acts as an important platform in transportation research. Considering the growing complexity of urban mobility, various large-scale regional simulators are designed and used for research and applications. Calibration is a key issue in the [...] Read more.
Traffic simulation, a tool for recreating real-life traffic scenarios, acts as an important platform in transportation research. Considering the growing complexity of urban mobility, various large-scale regional simulators are designed and used for research and applications. Calibration is a key issue in the traffic simulation: it finds the optimal system pattern to decrease the gap between the simulator output and the real data, making the system much more reliable. This paper proposes DRBO, a calibration framework for large-scale traffic simulators. This framework combines the travel behavior adjustment with black box optimization, better exploring the structure of the regional scale mobility. The motivation of the framework is based on the decomposition of the regional scale mobility dynamic. We decompose the mobility dynamic into the car-following dynamic and the routing dynamic. The prior dynamic imitates how vehicles propagate as time flows while the latter one reveals how vehicles choose their route according to their own information. Based on the decomposition, the DRBO framework uses iterative algorithms to find the best dynamic combinations. It utilizes the Bayesian optimization and day-to-day routing update to separately calibrate the dynamic, then combine them sequentially in an iterative way. Compared to the prior arts, the DRBO framework is efficient for capturing multiple perspectives of traffic conditions. We further tested our simulator on SFCTA demand to further validate the speed distribution from our simulation and observed data. Full article
(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)
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23 pages, 11258 KiB  
Article
Creating and Validating Hybrid Large-Scale, Multi-Modal Traffic Simulations for Efficient Transport Planning
by Fabian Schuhmann, Ngoc An Nguyen, Jörg Schweizer, Wei-Chieh Huang and Markus Lienkamp
Smart Cities 2025, 8(1), 2; https://doi.org/10.3390/smartcities8010002 - 24 Dec 2024
Cited by 1 | Viewed by 1344
Abstract
Mobility digital twins (MDTs), which utilize multi-modal microscopic (micro) traffic simulations and an activity-based demand generation, are envisioned as flexible and reliable planning tools for addressing today’s increasingly complex and diverse transport scenarios. Hybrid models may become a resource-efficient solution for building MDTs [...] Read more.
Mobility digital twins (MDTs), which utilize multi-modal microscopic (micro) traffic simulations and an activity-based demand generation, are envisioned as flexible and reliable planning tools for addressing today’s increasingly complex and diverse transport scenarios. Hybrid models may become a resource-efficient solution for building MDTs by creating large-scale, mesoscopic (meso) traffic simulations, using simplified, queue-based network-link models, in combination with more detailed local micro-traffic simulations focused on areas of interest. The overall objective of this paper is to develop an efficient toolchain capable of automatically generating, calibrating, and validating hybrid scenarios, with the following specific goals: (i) an automated and seamless merge of the meso- and micro-networks and demand; (ii) a validation procedure that incorporates real-world data into the hybrid model, enabling the meso- and micro-sub-models to be validated separately and compared to determine which simulation, micro- or meso-, more accurately reflects reality. The developed toolchain is implemented and applied to a case study of Munich, Germany, with the micro-simulation focusing on the city quarter of Schwabing, using real-word traffic flow and floating car data for validation. When validating the simulated flows with the detected flows, the regression curve shows acceptable values. The speed validation with floating car data reveals significant differences; however, it demonstrates that the micro-simulation achieves considerably better agreement with real speeds compared to the meso-model, as expected. Full article
(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)
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22 pages, 5373 KiB  
Article
A Predictive Compact Model of Effective Travel Time Considering the Implementation of First-Mile Autonomous Mini-Buses in Smart Suburbs
by Andres Udal, Raivo Sell, Krister Kalda and Dago Antov
Smart Cities 2024, 7(6), 3914-3935; https://doi.org/10.3390/smartcities7060151 - 11 Dec 2024
Viewed by 871
Abstract
An important development task for the suburbs of smart cities is the transition from rigid and economically inefficient public transport to the flexible order-based service with autonomous vehicles. The article proposes a compact model with a minimal input data set to estimate the [...] Read more.
An important development task for the suburbs of smart cities is the transition from rigid and economically inefficient public transport to the flexible order-based service with autonomous vehicles. The article proposes a compact model with a minimal input data set to estimate the effective daily travel time (EDTT) of an average resident of a suburban area considering the availability of the first-mile autonomous vehicles (AVs). Our example case is the Järveküla residential area beyond the Tallinn city border. In the model, the transport times of the whole day are estimated on the basis of the forenoon outbound trips. The one-dimensional distance-based spatial model with 5 residential origin zones and 6 destination districts in the city is applied. A crucial simplification is the 3-parameter sub-model of the distribution of distances on the basis of the real mobility statistics. Effective travel times, optionally completed with psycho-physiological stress factors and psychologically perceived financial costs, are calculated for all distances and transportation modes using the characteristic speeds of each mode of transport. A sub-model of switching from 5 traditional transport modes to two AV-assisted modes is defined by an aggregated AV acceptance parameter ‘a’ based on resident surveys. The main output of the model is the EDTT, dependent on the value of the parameter a. Thanks to the compact and easily adjustable set of input data, the main values of the presented model are its generalizability, predictive ability, and transferability to other similar suburban use cases. Full article
(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)
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20 pages, 1304 KiB  
Article
Robust Reinforcement Learning Strategies with Evolving Curriculum for Efficient Bus Operations in Smart Cities
by Yuhan Tang, Ao Qu, Xuan Jiang, Baichuan Mo, Shangqing Cao, Joseph Rodriguez, Haris N Koutsopoulos, Cathy Wu and Jinhua Zhao
Smart Cities 2024, 7(6), 3658-3677; https://doi.org/10.3390/smartcities7060141 - 29 Nov 2024
Viewed by 1274
Abstract
Public transit systems are critical to the quality of urban life, and enhancing their efficiency is essential for building cost-effective and sustainable smart cities. Historically, researchers sought reinforcement learning (RL) applications to mitigate bus bunching issues with holding strategies. Nonetheless, these attempts often [...] Read more.
Public transit systems are critical to the quality of urban life, and enhancing their efficiency is essential for building cost-effective and sustainable smart cities. Historically, researchers sought reinforcement learning (RL) applications to mitigate bus bunching issues with holding strategies. Nonetheless, these attempts often led to oversimplifications and misalignment with the goal of reducing the total time passengers spent in the system, resulting in less robust or non-optimal solutions. In this study, we introduce a novel setting where each bus, supervised by an RL agent, can appropriately form aggregated policies from three strategies (holding, skipping station, and turning around to serve the opposite direction). It’s difficult to learn them all together, due to learning complexity, we employ domain knowledge and develop a gradually expanding action space curriculum, enabling agents to learn these strategies incrementally. We incorporate Long Short-Term Memory (LSTM) in our model considering the temporal interrelation among these actions. To address the inherent uncertainties of real-world traffic systems, we impose Domain Randomization (DR) on variables such as passenger demand and bus schedules. We conduct extensive numerical experiments with the integration of synthetic and real-world data to evaluate our model. Our methodology proves effective, enhancing bus schedule reliability and reducing total passenger waiting time by over 15%, thereby improving bus operation efficiency and smoothering operations of buses that align with sustainable goals. This work highlights the potential of robust RL combined with curriculum learning for optimizing public transport in smart cities, offering a scalable solution for real-world multi-agent systems. Full article
(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)
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