Demand Exploration of Automated Mobility On-Demand Services Using an Innovative Simulation Tool

The prospect of automated mobility on-demand (AMoD) services in urban areas has highlighted critical challenges surrounding the sustainable development of urban mobility. To address this gap, we created an improved simulation tool by combining SimMobility’s demand simulator with the hybrid meso-micro supply model of Aimsun Next. A realistic and city-scale examination of AMoD using such an advanced simulation tool can address the limitations identified in the literature and significantly promote the understanding of AMoD services. In this paper, we demonstrate the use of our improved simulation tool and focus on demand exploration of AMoD services in the Tel-Aviv metropolitan area. We employ an activity- and agent-based framework, for both single and shared AMoD rides, and explore 6 service cost scenarios and its impact on demand elasticities, mode choice, travel patterns and AMoD use by population groups. Our results indicate that there is no existing latent demand in Tel-Aviv metropolitan area and the extent of mode shifts from active modes and public transportation to AMoD is neglectable. This is due to AMoD services average travel costs, which is high as compared to all other modes, even with the largest fare reduction examined. Furthermore, it was found that AMoD demand, as a single service, is more elastic than when AMoD is shared, as cost elasticities drops as fare reduction increases. Unlike other modes of transportation, the maximum number of AMoD trips is obtained for trips between 10 to 20 kilometers, while young riders and full-time students are responsible for most of AMoD trips

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

| openaire: EC/H2020/856602/EU//FINEST TWINS Funding Information: Funding: This research was funded by the FINEST Twins Center of Excellence, H2020 European Union funding for Research and Innovation grant number 856602. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.Peer reviewe

Daily Activity Schedule Tel Aviv 2040 from SimMobility MIT Preday

<p>This database countain the activities conducted in the Tel Aviv metropolis on a typical day in 2040. The information is derived from the outcomes of the Simobility demand simulator, which operates on synthetic population and land-use inputs specific to the Tel Aviv metropolis predictions.</p><p> </p&gt

Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas

We build on recent work to develop a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages person-trajectories obtained from an activity-based model calibrated for two full-scale prototype cities, consisting of representative synthetic populations and mobility networks for two contrasting auto-dependent city typologies. We simulate the propagation of the COVID-19 epidemic in both cities to analyze spreading patterns in urban networks across various activity types. Investigating the impact of the transit network, we find that its removal dampens disease propagation significantly, suggesting that transit restriction is more critical for mitigating post-peak disease spreading in transit dense cities. In the latter stages of disease spread, we find that the greatest share of infections occur at work locations. A statistical analysis of the resulting activity-based contact networks indicates that transit contacts are scale-free, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We validate our simulation results against existing case and mortality data across multiple cities in their respective typologies. Our framework demonstrates the potential for tracking epidemic propagation in urban networks, analyzing socio-demographic impacts and assessing activity- and mobility-specific implications of both non-pharmaceutical and pharmaceutical intervention strategies.ISSN:2045-232

Individual Table for Tel Aviv 2040 in SimMobility MIT Preday

<p>This table offers insights into the synthetic population of the Tel Aviv Metropolis projected for 2040. It serves as a forecast derived from various predictions by Israeli government officials. Primarily designed for research purposes, it is especially intended to be utilized as input for the SimMobility MIT  demand simulator.</p&gt

Sustainable Automated Mobility-On-Demand Strategies in Dense Urban Areas: A Case Study of the Tel Aviv Metropolis in 2040

The emergence of automated mobility-on-demand (AMoD) services in urban regions has underscored crucial issues concerning the sustainable advancement of urban mobility. In particular, the impact of various AMoD implementation strategies in dense, transit-oriented cities has yet to be investigated in a generalized manner. To address this gap, we quantify the effects of AMoD on trip patterns, congestion, and energy and emissions in a dense, transit-oriented prototype city via high-fidelity simulation. We employ an activity- and agent-based framework, with specific demand and supply considerations for both single and shared AMoD rides. Our findings suggest that, in densely populated, transit-oriented cities such as the Tel Aviv metropolis, AMoD contributes to higher congestion levels and increased passenger vehicle kilometers traveled (VKT). However, when AMoD is integrated with public transit systems or introduced alongside measures to reduce household car ownership, it helps alleviate the VKT impact. Furthermore, these combined approaches effectively counter the negative impact of AMoD on public transit ridership. None of the AMoD strategies analyzed in our study reduce the congestion effects of AMoD and all strategies cannibalize active mobility in dense, transit-oriented cities compared to the base case. Nevertheless, our analysis reveals that a policy leading to decreased car ownership proves to be a more efficient measure in curbing energy consumption and greenhouse gas emissions

Large-Scale Mobile-Based Analysis for National Travel Demand Modeling

Mobile phones have achieved a high rate of penetration and gained great interest in the field of travel behavior studies. However, mobile phone data exploitation for national travel models has only been sporadically studied thus far. This work focuses on one of the most extensive cellular surveys of its kind carried out thus far in the world, which was performed for two years between 2018 and 2019 with the participation of the two largest cellular providers in Israel, as well as leading GPS companies. The large-scale cell phone survey covered half the population using cellphones aged 8+ in Israel and uncovered local and national trip patterns, revealing the structure of nationwide travel demand. The methodology consists of the following steps: (1) plausibility and quality checks for the data of the mobile operators and the GPS data providers; (2) algorithm development for trip detection, home/work location detection, location and time accuracy, and expansion factors; (3) accuracy test of origin–destination matrices at different resolutions, revisions of algorithms, and reproduction of data; and (4) validation of results by comparison to reliable external data sources. The results are characterized by high accuracy and representativeness of demand and indicate a strong correlation between the cellular survey and other reliable sources

A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models

| openaire: EC/H2020/856602/EU//FINEST TWINSAddressing complexity in transportation in cases such as disruptive trends or disaggregated management strategies has become increasingly important. This in turn is resulting in the rising adoption of Agent-Based and Activity-Based modeling. Still, a broad adoption is hindered by the high complexity and computational needs. For example, hundreds of parameters are involved in the calibration of Activity-Based models focused on behavioral theory, to properly frame the required detailed socio-economical characteristics. To address this challenge, this paper presents a novel Bayesian Optimization approach that incorporates a surrogate model defined as an improved Random Forest to automate the calibration process of the behavioral parameters. The presented solution calibrates the largest set of parameters yet, according to the literature, by combining state-of-the-art methods. To the best of the authors’ knowledge, this is the first work in which such a high dimensionality is tackled in sequential model-based algorithm configuration theory. The proposed method is tested in the city of Tallinn, Estonia, for which the calibration of 477 behavioral parameters is carried out. The calibration process results in a satisfactory performance for all the major indicators, the OD matrix average mismatch is equal to 15.92 vehicles per day while the error for the overall number of trips is equal to 4%.Peer reviewe

Examining impacts of time-based pricing strategies in public transportation: A study of Singapore

Peak and off-peak pricing strategies are an important policy tool used to spread peak demand in public transportation systems. This study uses an agent-based simulator (SimMobility Mid-term) to examine the impact of pricing (off-peak fare discounts) strategies used in Singapore. The aim of the paper is to demonstrate the capabilities of the simulator, and types of detailed performance indicators it can provide, in order to examine the effects of complex public transport pricing policies. Behavioral models within the simulator are calibrated with relevant datasets such as household travel survey, smart card, GPS probe data from taxis and traffic counts for the Singapore network. Nine (09) time-based pricing strategies are examined that consist of a combination of free pre-peak travel on Mass Rapid Transit (MRT) and an off-peak discount for integrated transit (public buses, MRT and Light Rail Transit (LRT)). Changes in public transport ridership, mode shares, operator\u27s revenue and denied boarding are used as indicators to examine the impacts of pricing strategies. The effects of these policies are also examined on segments of the population in terms of income level, person type and gender. Results indicate that off-peak discounts spread PM peak demand and attract individuals to public transportation. However, the availability of fare discounts in all off-peak periods results in adverse impacts during the AM peak because many commuters shift the return leg rather than the initial leg of their journey. The study concludes with suggestions on how to explore more effective pricing strategies, i.e. providing fare discounts only during off-peak periods that surround AM peak