Bicycle superhighway: An environmentally sustainable policy for urban transport

Bicycle is a sustainable low-carbon transport mode. However, insufficient or unplanned infrastructure leads to decrease in the share of bicycle in many cities of developing nations. In order to increase the bicycle share and to provide safer, faster and more direct routes, a bicycle superhighway is proposed for urban areas. This study identifies the potential of increase in the bicycle share. For maximum utilization of the new infrastructure, an algorithm is presented to identify the optimum number and locations of the connectors between proposed new infrastructure and existing network. Household income levels are incorporated into the decision making process of individual travellers for a better understanding of the modal shift. A real-world case study of Patna, India is chosen to show the application of the proposed superhighway. It is shown that for Patna, the bicycle share can escalate as high as 48% up from 32% by providing this kind of infrastructure. However, together with bicycles, allowing motorbikes on the superhighway limits the bicycle share to 44%. The increase in bicycle share is mainly a result of people switching from motorbike, public transport and walk to the bicycle. Further, to evaluate the benefits of the bicycle superhighway, this study first extends an emission modelling tool to estimate the time-dependent, vehicle-specific emissions under mixed traffic conditions. Allowing only bicyclists on the superhighway improves congested urban areas, reduces emissions, and increases accessibility. However, allowing motorbikes on the superhighway increases emissions significantly in the central part of the urban area and reduces accessibilities by bicycle mode to education facilities which are undesirable. This study elicits that a physically segregated high-quality bicycle superhighway will not only attract current non-cyclist travellers and increase the share of the bicycle mode, but will also reduce negative transport externalities significantly

Disaggregate path flow estimation in an iterated DTA microsimulation

This text describes the first application of a novel path flow and origin/destination (OD) matrix estimator for iterated dynamic traffic assignment (DTA) microsimulations. The presented approach, which operates on a trip-based demand representation, is derived from an agent-based DTA calibration methodology that relies on an activity-based demand model (Flötteröd et al., 2011a). The objective of this work is to demonstrate the transferability of the agent-based approach to the more widely used OD matrix-based demand representation. The calibration (i) operates at the same disaggregate level as the microsimulation and (ii) has drastic computational advantages over conventional OD matrix estimators in that the demand adjustments are conducted within the iterative loop of the DTA microsimulation, which results in a running time of the calibration that is in the same order of magnitude as a plain simulation. We describe an application of this methodology to the trip-based DRACULA microsimulation and present an illustrative example that clarifies its capabilities

An agent-based implementation of freight receiver and carrier collaboration with cost sharing

Freight transport stakeholders can benefit from collaborative planning. Unfortunately, appropriate decision and planning support tools are lacking. Consequently, freight stakeholders remain unaware of collaboration opportunities and the potential benefit of those coalitions. This paper focuses on implementing collaboration between urban freight receivers and carriers. Collaboration takes the form of cost-sharing among coalition members when receivers are willing to extend their time windows. Rigorous experiments confirm the behavioural sensitivity of the model. A realistically-sized case study in the City of Cape Town, South Africa, demonstrates the usability of the agent-based simulation model. The case study considers the impact of collaboration on after-hour deliveries. Results indicate that delivery cost reduces significantly (nearly 30%) when carriers and receivers are willing to collaborate and adopt after-hour deliveries - the carrier’s fleet composition changes to favour fewer but larger vehicles.https://www.sciencedirect.com/journal/transportation-research-interdisciplinary-perspectivespm2022Industrial and Systems Engineerin

The Multi-Agent Transport Simulation MATSim

"The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations.The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.

Congestion pricing in a world of self-driving vehicles: An analysis of different strategies in alternative future scenarios

The introduction of autonomous (self-driving) and shared autonomous vehicles (AVs and SAVs) will affect travel destinations and distances, mode choice, and congestion. From a traffic perspective, although some congestion reduction may be achieved (thanks to fewer crashes and tighter headways), car-trip frequencies and vehicle miles traveled (VMT) are likely to rise significantly, reducing the benefits of driverless vehicles. Congestion pricing (CP) and road tolls are key tools for moderating demand and incentivizing more socially and environmentally optimal travel choices. This work develops multiple CP and tolling strategies in alternative future scenarios, and investigates their effects on the Austin, Texas network conditions and traveler welfare, using the agent-based simulation model MATSim. Results suggest that, while all pricing strategies reduce congestion, their social welfare impacts differ in meaningful ways. More complex and advanced strategies perform better in terms of traffic conditions and traveler welfare, depending on the development of the mobility landscape of autonomous driving. The possibility to refund users by reinvesting toll revenues as traveler budgets plays a salient role in the overall efficiency of each CP strategy as well as in the public acceptability

Evolutionary design of sustainable mobility and transport system（進化計算を用いた持続可能なモビリティと輸送システムの設計）

信州大学(Shinshu university)博士（工学)ThesisARMAS ANDRADE TITO ROLANDO. Evolutionary design of sustainable mobility and transport system（進化計算を用いた持続可能なモビリティと輸送システムの設計）. 信州大学, 2018, 博士論文. 博士（工学), 甲第686号, 平成30年03月20日授与.doctoral thesi

Agent-based transport demand modelling for the South African commuter environment

Past political regimes and socio-economic imbalances have led to the formation of a transport system in the Republic of South Africa (RSA) that is unique to the developing world. Affluent communities in metropolitan cities are situated close to economic activity, whereas the people in need of public transport are situated on the periphery of the cities. This demographic structure is opposite to that of developed countries and complicates both the provision of transport services and the planning process thereof. Multi-Agent Transport Simulation (MATSim) has been identified as an Agent-Based Simulation (ABS) approach that models individual travellers as autonomous entities to create large scale traffic simulations. The initial implementation of MATSim in the RSA successfully simulated private vehicle trips between home and work in the province of Gauteng, proving that there is enough data available to create a realistic multi-agent transport model. The initial implementation can be expanded to further enhance the simulation accuracy, but this requires the incorporation of additional primary and secondary activities into the initial transport demand. This study created a methodology to expand the initial implementation in the midst of limited data, and implemented this process for Gauteng. The first phase constructed a 10% synthetic population that represents the demographic structure of the actual population and identified various socio-demographic attributes that can influence an individual's travel behaviour. These attributes were assigned to the synthetic agents by following an approach that combines probabilistic sampling and rule-based models. The second phase used agents' individual attributes, and census, National Household Travel Survey (NHTS) and geospatial data to transform the synthetic population into a set of daily activity plans - one for every agent. All the agents' daily plans were combined into a plans.xml file that was used as input to MATSim, where the individuals' activity plans were executed simultaneously to model the transport decisions and behaviour of agents. Data deficiencies were overcome by contemplating various scenarios and comparing the macroscopic transport demand patterns thereof to the results of the initial implementation and to actual counting station statistics. This study successfully expanded the initial home-work-home implementation of MATSim by including additional non-work activities in the transport demand. The addition of non-work activities improved the simulation accuracy during both peak and off-peak periods, and the initial demand therefore provides an improved representation of the travel behaviour of individuals in Gauteng.Dissertation (MEng)--University of Pretoria, 2011.Industrial and Systems Engineeringunrestricte

Computationally efficient offline demand calibration algorithms for large-scale stochastic traffic simulation models

Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 168-181).This thesis introduces computationally efficient, robust, and scalable calibration algorithms for large-scale stochastic transportation simulators. Unlike a traditional "black-box" calibration algorithm, a macroscopic analytical network model is embedded through a metamodel simulation-based optimization (SO) framework. The computational efficiency is achieved through the analytical network model, which provides the algorithm with low-fidelity, analytical, differentiable, problem-specific structural information and can be efficiently evaluated. The thesis starts with the calibration of low-dimensional behavioral and supply parameters, it then addresses a challenging high-dimensional origin-destination (OD) demand matrix calibration problem, and finally enhances the OD demand calibration by taking advantage of additional high-resolution traffic data. The proposed general calibration framework is suitable to address a broad class of calibration problems and has the flexibility to be extended to incorporate emerging data sources. The proposed algorithms are first validated on synthetic networks and then tested through a case study of a large-scale real-world network with 24,335 links and 11,345 nodes in the metropolitan area of Berlin, Germany. Case studies indicate that the proposed calibration algorithms are computationally efficient, improve the quality of solutions, and are robust to both the initial conditions and to the stochasticity of the simulator, under a tight computational budget. Compared to a traditional "black-box" method, the proposed method improves the computational efficiency by an average of 30%, as measured by the total computational runtime, and simultaneously yields an average of 70% improvement in the quality of solutions, as measured by its objective function estimates, for the OD demand calibration. Moreover, the addition of intersection turning flows further enhances performance by improving the fit to field data by an average of 20% (resp. 14%), as measured by the root mean square normalized (RMSN) errors of traffic counts (resp. intersection turning flows).by Chao Zhang.Ph. D. in Transportatio