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The present study contributes to narrowing down the research gap in modeling individual door-to-door trips in a superblock scenario and in evaluating the respective impacts in terms of travel times, modal shifts, traffic performance, and environmental benefits. The methods used are a multiple-criteria approach to identify the superblocks and a large-scale, multi-model, activity-based microscopic simulation. These methods were applied to the city of Bologna, Italy, where 49 feasible superblocks were identified. A previous large-scale microscopic traffic model of Bologna is leveraged to build a baseline scenario. A superblock scenario is then created to model five proposed traffic intervention measures. Several mobility benefit indicators at both citywide and superblock levels are compared. The simulation results indicate a significant increase in walking time for car drivers, while the average waiting time of bus users decreases due to the increased frequency of bus services. This leads to a noticeable car-to-bus shift. In addition, absolute traffic volumes and traffic-related emissions decreased significantly. Surprisingly, traffic volumes on the roads around the superblocks did not increase as expected. In general, this research provides scientists and urban and transport planners with insights into how changes in door-to-door travel times of multi-modal trips can impact individual travel behavior and traffic performance at a citywide level. However, the study still has limitations in modeling the long-term effects regarding changing activity locations within the superblocks. [ABSTRACT FROM AUTHOR]

This paper analyzes the relation between Shannon's information entropy and trips made by cyclists in an urban context. Such entropy indicator depends on people distribution across a territory in a specific time. Its evaluation and temporal evolution have multiple applications in transport planning, from the specification of demand distribution models to the calibration and validation of transport demand models to be used where transport data are scarce. This feature is crucial in transport planning, as the lack of robust data to estimate travel demand is still a common challenge, especially for cycling mobility. The entropy indicator has been applied to the cyclists' case in the Italian city of Bologna, using approximately 165,000 GPS traces recorded from April to September 2017. The results are encouraging because the entropy indicator is able to explain the evolution of the agents' distribution throughout a day across the territory. However, it is suggested that future research may further deepen the concept to test its transferability, and to better establish its sensibility to other case studies. [ABSTRACT FROM AUTHOR]

This article presents an analysis of the supply and demand of a FFBSS recently implemented in the city of Bologna, Italy. The main aspects treated in this paper are: analysis of bike availability; temporal analysis of FFBSS demand; calibration and validation of a novel model that predicts the number of daily trips per available bike. This model is based on a linear combination of several day attributes, including meteorological and day-type attributes. Moreover, an origin to destination analysis is generated showing the spatial distribution of FFBSS trips. The methods are applied to a scenario with almost a million GPS traces recorded between July and October 2018 by the FFBSS in Bologna. Findings could support FFBSS companies to better understand the fluctuation of both the transport demand and supply of this relatively recent transport mode, as to make more efficient decisions when distributing or relocating bicycles. [ABSTRACT FROM AUTHOR]

This paper seeks to predict the average waiting time, defined as the time spent moving at 1 ms − 1 or less, of urban bicyclists during rush hours while performing different maneuvers at intersections. Individual predictive models are built for the three cyclist typologies previously identified on a large database of GPS traces recorded in the city of Bologna, Italy. Individual models are built for the three cyclist typologies and bootstrapping has confirmed the validity and robustness of the results. The results allow the integration of waiting times in route choice models for cyclists, thus improving the rational bases by which cyclists makes their decisions. Moreover, the modeling allows transportation engineers to understand how different cyclist typologies perceive different variables that affect their waiting times. Future work should focus on testing the model transferability to other case studies. [ABSTRACT FROM AUTHOR]

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources. [ABSTRACT FROM AUTHOR]

This research describes numerical methods to analyze the absolute transport demand of cyclists and to quantify the road network weaknesses of a city with the aim to identify infrastructure improvements in favor of cyclists. The methods are based on a combination of bicycle counts and map-matched GPS traces. The methods are demonstrated with data from the city of Bologna, Italy: approximately 27,500 GPS traces from cyclists were recorded over a period of one month on a volunteer basis using a smartphone application. One method estimates absolute, city-wide bicycle flows by scaling map-matched bicycle flows of the entire network to manual and instrumental bicycle counts at the main bikeways of the city. As there is a fairly high correlation between the two sources of flow data, the absolute bike-flows of the entire network have been correctly estimated. Another method describes a novel, total deviation metric per link which quantifies for each network edge the total deviation generated for cyclists in terms of extra distances traveled with respect to the shortest possible route. The deviations are accepted by cyclists either to avoid unpleasant road attributes along the shortest route or to experience more favorable road attributes along the chosen route. The total deviation metric indicates to the planner which road links are contributing most to the total deviation of all cyclists. In this way, repellant and attractive road attributes for cyclists can be identified. This is why the total deviation metric is of practical help to prioritize bike infrastructure construction on individual road network links. Finally, the map-matched traces allow the calibration of a discrete choice model between two route alternatives, considering distance, share of exclusive bikeway, and share of low-priority roads. [ABSTRACT FROM AUTHOR]