Traffic Simulation with Aimsun

Casas, Jordi; Ferrer, Jaime; García, Dominie; Perarnau, Josep; Torday, Alexandre

doi:10.1007/978-1-4419-6142-6_5

Cited by 135 publications

(82 citation statements)

References 24 publications

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“…A highly-detailed traffic model was built in AIMSUN 8.1.2 model (Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks) for this case study to replicate and estimate traffic conditions and its related environmental and operational effects. AIMSUN is a microscopic traffic simulation model that can simulate the individual behavior of a vehicle on the network over time and in accordance to the various theories of vehicle behavior, namely the Gipps car-following [24], lane-changing [25] and gap acceptance models [26]. This model was developed to be a tool to support traffic engineers so that they can analyze and design traffic models.…”

Section: Methodsmentioning

confidence: 99%

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Evaluating the impacts of using cargo cycles on urban logistics: integrating traffic, environmental and operational boundaries

Melo

Baptista

2017

Eur. Transp. Res. Rev.

126

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Introduction European Commission has promoted actions and policies with the aim of reducing the negative impacts on traffic and environment caused by city logistics. One increasingly popular measure is the use of cargo bikes in city logistics due to their improved energy efficiency, lower emissions and lower traffic disturbance. The paper assess the impacts of electric cargo bikes, from a public policy perspective and, simultaneously, taking into account variables that cover the urban logistics operators' interests. Under a public policy perspective, the considered variables evaluate mobility, environmental impacts and indirectly, the quality of life. In terms of private interests, the studied variables cover costs levels (operation and driving) and efficiency. This evaluation aims at clarifying if electric cargo bikes can indeed represent a sustainable mobility policy under specific boundaries, by leading to better environmental and social impacts and not hindering the operational efficiency of urban logistics activities. Methods For that purpose, the measurement of the traffic key performance indicators, as well as of Well-to-Wheel energy and CO 2 emission savings is performed allowing to quantify mobility, reliability and operational efficiency indicators. Several scenarios related with the introduction of electric cargo bikes replacing conventional vans were assessed and evaluated in order to compare the effects of different market shares in the mobility of the studied area located in Porto (Portugal). Acknowledging the short distance range of cargo bikes, the simulation is carried out and an estimation of the total transportation cost is performed, which includes transport and emission cost when the vehicle is driving, emission cost while idling and labor cost. Results The main conclusion is that cargo bikes can replace up to 10% of the conventional vans in areas with maximum linear distances of about 2 km, without changing the overall network efficiency. Additionally, urban logistics WTW CO 2 emission impacts can be reduced by up to 73%, which represents 746 kg of CO 2 avoided emissions. Furthermore, the introduction of electric cargo bikes in urban logistics activities has positive effects for all the vehicle categories and all the scenarios, reaching up to 25% of reductions in external costs. Conclusions As a result, cargo bikes implemented at the appropriate spatial scale within the city can be a sustainable solution for urban logistics, depending on a prior delimitation of the conditions and adequate implementation strategy, to guarantee that it actually leads to improvements in terms of mobility, environment, energy, running costs and externalities.

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Section: Methodsmentioning

confidence: 99%

“…Further technical details about the AIMSUN and its capabilities can be found in Barceló [28] and Casas [26]. Taking advantage of the AIMSUN potentialities, several studies on urban logistics have been recently developed using this simulation tool [3,4,[29][30][31][32].…”

Section: Methodsmentioning

confidence: 99%

Evaluating the impacts of using cargo cycles on urban logistics: integrating traffic, environmental and operational boundaries

Melo

Baptista

2017

Eur. Transp. Res. Rev.

126

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“…The emission of CO 2 is (for a given type of fuel) directly proportional to the fuel consumption. All response variables have been evaluated with the help of the microscopic traffic simulator AIMSUN v. 5.1.11 [3]. Fuel consumption rates and pollution emissions have been derived with the help of AIMSUN's environmental models.…”

Section: Methodsmentioning

confidence: 99%

Self-Organised Routing for Road Networks

Prothmann

Tomforde

Lyda

et al. 2012

Self-Organizing Systems

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“…Models of this class aim to specify a safe following distance behind the leader vehicle. Gipps' model is mostly known for being the building block of the Aimsun microscopic simulator (Casas et al, 2010). It consists of two components: acceleration and deceleration sub-models, corresponding to the empirical formulations illustrated by equations (1) and (2), which output the speed of each vehicle at a given time t in terms of its speed at the previous step.…”

Section: The Gipps Car-following Modelmentioning

confidence: 99%

“…In fact, according to Hollander and Liu (2008), when the calibration problem is formulated as an optimization problem, it is unlikely to lead to a global optimum, due to the multidimensionality of the solution search space and the tendency of the observed data to exhibit various inconsistencies. Casas et al (2010) state that the most exact procedure to calibrate the car-following model is to conduct specific experiments in which accurate field data is recorded on the relative distances and speeds between pairs of leaderfollower vehicles, and the simulation model is calibrated against this field data. Trajectory data can be obtained using instrumented vehicles (Brackstone et al, 2002;Ranjitkar et al, 2005) or from aerial images, collected either from tall buildings (Punzo et al, 2011) or from helicopters (Ossen and Hoogendoorn, 2005).…”

Section: Introductionmentioning

confidence: 99%

Calibration of the Gipps Car-following Model Using Trajectory Data

Vasconcelos

Neto

Santos

et al. 2014

Transportation Research Procedia

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One of the most important tasks in the microscopic simulation of traffic flow, assigned to the car following sub-model, is the modelling of the longitudinal movement of vehicles. The calibration of a car-following model is usually done at an aggregated level, using macroscopic traffic stream variables (speed, flow, density). There is an interest in calibration procedures based on disaggregated data. However, obtaining accurate trajectory data is a real challenge. This paper presents a low-cost procedure to calibrate the Gipps car-following model. The trajectory data is collected with a car equipped with a datalogger and a LIDAR rangefinder. The datalogger combines GPS and accelerometers data to provide accurate speed and acceleration measurements. The LIDAR measures the distances to the leading or following vehicle. Two alternative estimation methods were tested: the first follows individual procedures that explicitly account for the physical meaning of each parameter; the second formulates the calibration as an optimization problem: the objective function is defined so as to minimize the differences between the simulated and real inter-vehicle distances; the problem is solved using an automated procedure based on a genetic algorithm. The results show that the optimization approach leads to a very accurate representation of the specific modeled situation but offers poor transferability; on the other hand, the individual estimation provides a satisfactory fit in a wide range of traffic conditions and hence is the recommended method for forecasting purposes.

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Traffic Simulation with Aimsun

Cited by 135 publications

References 24 publications

Evaluating the impacts of using cargo cycles on urban logistics: integrating traffic, environmental and operational boundaries

Evaluating the impacts of using cargo cycles on urban logistics: integrating traffic, environmental and operational boundaries

Self-Organised Routing for Road Networks

Calibration of the Gipps Car-following Model Using Trajectory Data

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