Cost-Effective Transportation Planning for Smart Cities

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

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

Title: Integrating Multimodal Data Sources for Cost-Effective Transportation Planning in San Francisco Using LPSim
Authors: Xuan Jiang; Junzhe Cao; Raja Sengupta
Affiliation: University of California, Berkeley
Abstract: This paper focuses on integrating multimodal data sources for cost-effective transportation planning in San Francisco using LPSim. We sourced edge (road segment) and node (intersection) data from OpenStreetMap (OSM), providing a comprehensive and up-to-date route network for the San Francisco Bay Area. The dataset includes 223,327 nodes and 547,696 edges within the region. Additionally, we obtained General Transit Feed Specification (GTFS) data from the San Francisco Municipal Transportation Agency (SFMTA), which operates an extensive network of fuel-efficient Muni buses, light rail Metro trains, historic streetcars, and cable cars. To enhance the simulation, we incorporated data from the San Francisco Chained Activity Modelling Process (SF-CHAMP), generating origin and destination information for over 20 million trips. The simulation was conducted using an extended version of LPSim, which includes transit data. Our results demonstrate the distribution of travel times, transit boardings and alightings, and the potential impact on Vehicle Miles Traveled (VMT). This approach offers insights into optimizing transportation strategies for smart cities, minimizing costs, and reducing environmental impacts.

Title: Creating Hybrid Traffic Simulations for Efficient Transport Planning: A Case Study of Munich
Authors: Fabian Schuhmann; Wei-Chieh Huang; Ngoc An Nguyen; Jörg Schweizer; Markus Lienkamp
Affiliation: Technische Universität München; University of Bologna
Abstract: Mobility Digital Twins (MDT) using multi-modal microscopic traffic simulations with an activity-based demand generation pretend to be reliable planning tools, as any thinkable transport scenario can be constructed to produce the most realistic results. However, building such MDT for larger urban areas is resource-intensive in terms of modeling efforts, data requirements, computing resources, simulation execution times, and calibration challenges. This is most likely the main reason why only a few such models exist, and even fewer have been validated, despite their potential as a flexible and precise planning tool. Large-scale mesoscopic traffic simulations, with simplified, queue-based network-link models, have been used in combination with smaller microscopic traffic simulations, called hybrid models, to deliver a more resource-efficient yet detailed simulation in an area of interest. Again, despite various efforts to build such hybrid models, practically no study can be found in the literature with validated results proving that the simulated traffic of the baseline scenario is close to real observations. The first objective of this paper is to present an effective toolchain that builds a hybrid model where the traffic is simulated on a microscopic scale within a larger mesoscopic network for which the daily demand has been generated with an activity-based demand model. The toolchain includes all processes, from the construction of the mesoscopic and microscopic network model, plan generation, determination of user equilibrium through the mesoscopic simulation, and the microscopic simulation of a final evaluation. A particular problem of hybrid models is to match the results of the mesoscopic simulation to the microscopic network. In this paper, a unique solution to this problem is presented, where the typically smaller but more refined microscopic network is blended into the mesoscopic network. In this way, the network links of the mesoscopic and the microscopic network are identical within the microscopic network present. The identity of mesoscopic and microscopic network links ensures the consistency of both models in terms of demand transfer and link-based validation. The second objective is to validate the hybrid model by means of a case study with both flow and floating car data. The proposed toolchain is demonstrated by a case study of the city of Munich, Germany: the mesoscopic network comprises the larger Munich area, whereas the microscopic network covers the city quarter of Schwabing. In the present work, detected flows and speeds from floating cars are matched to the network links for validation. A car-only and a multi-modal scenario have been investigated. The validation of the flows shows acceptable values using standard metrics, whereas the speed validation shows significant differences, especially in the mesoscopic model. In conclusion, the hybrid approach presented in this paper opens the perspective to use realistic and flexible methods for the planning process that can be validated and that require fewer resources compared to pure microscopic approaches.

Title: DRBO - A Regional Scale Simulator Calibration Framework based on day-to-day Dynamic Routing and Bayesian Optimization
Authors: Xuan Jiang; Chonghe Jiang; Junzhe Cao; Alexander Skabardonis; Alex Kurzhanskiy; Raja Sengupta
Affiliation: Civil and Environmental Engineering at UC Berkeley; The Chinese University of Hong Kong; University of California; Lawrence Berkeley National Laboratory
Abstract: Efficient transportation strategies are critical for minimizing implementation costs and reducing the environmental and societal impacts of urban mobility systems. In this paper, we present DRBO, a scalable framework for calibrating regional traffic simulators that balances cost-efficiency with improved transport system reliability. The DRBO framework decomposes the complexity of regional traffic dynamics into two key components: vehicle flow dynamics and dynamic routing behavior. Through an iterative approach combining Bayesian optimization with day-to-day routing updates, the framework optimally adjusts travel behaviors to close the gap between simulated outputs and real-world data. By improving the accuracy of traffic simulations, DRBO provides a valuable tool for transportation agencies and researchers to evaluate policies, enhance traffic flow, and optimize resource allocation. We demonstrate the efficacy of DRBO through a case study using the San Francisco County Transportation Authority’s (SFCTA) demand model, achieving improved accuracy in speed distribution predictions compared to prior methods.