scholarly journals How Do People Cycle in Amsterdam, Netherlands?: Estimating Cyclists’ Route Choice Determinants with GPS Data from an Urban Area

2017 ◽  
Vol 2662 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Danique Ton ◽  
Oded Cats ◽  
Dorine Duives ◽  
Serge Hoogendoorn
Keyword(s):  
The Netherlands ◽  
Inner City ◽  
Urban Area ◽  
Choice Model ◽  
Route Choice ◽  
Urban Environments ◽  
Gps Data ◽  
Data Set ◽  
The Impact ◽  
Cycle Path

Nowadays, the bicycle is seen as a sustainable and healthy substitute for the car in urban environments. The Netherlands is the leading country in bicycle use, especially in urban environments. Yet route choice models featuring inner-city travel that includes cyclists are lacking. This study estimated a cyclists’ route choice model for the inner city of Amsterdam, Netherlands, on the basis of 3,045 trips collected with GPS data. The main contribution of this study was the construction of the choice set with an empirical approach, which used only the observed trips in the data set to compose the choice alternatives. The findings suggested that cyclists were insensitive to separate cycle paths in Amsterdam, a city characterized by a dense cycle path network in which cycling was the most prominent mode of travel. In addition, cyclists were found to minimize travel distance and the number of intersections per kilometer. The impact of distance on route choice increased during the morning peak when schedule constraints were more prevalent. Furthermore, overlapping routes were more likely to be chosen by cyclists, everything else being the same.

The Impact of Traffic Information on Drivers’ Day-to-Day Route Choice Behavior

2011 ◽  
Vol 97-98 ◽  
pp. 925-930
Author(s):  
Shi Xu Liu ◽  
Hong Zhi Guan
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Statistical Characteristics ◽  
Traffic Information ◽  
Route Choice Behavior ◽  
The Impact

The influence of different traffic information on drivers’ day-to-day route choice behavior based on microscopic simulation is investigated. Firstly, it is assumed that drivers select routes in terms of drivers’ perceived travel time on routes. Consequently, the route choice model is developed. Then, updating the drivers’ perceived travel time on routes is modeled in three kinds of traffic information conditions respectively, which no information, releasing historical information and releasing predictive information. Finally, by setting a simple road network with two parallel paths, the drivers’ day-to-day route choice is simulated. The statistical characteristics of drivers’ behavior are computed. Considering user equilibrium as a yardstick, the effects of three kinds of traffic information are compared. The results show that the impacts of traffic information on drivers are related to the random level of driver’s route choice and reliance on the information. In addition, the road network cannot reach user equilibrium in three kinds of information. This research results can provide a useful reference for the application of traffic information system.

Route-Choice Modeling for Pedestrian Evacuation Based on Infrastructure Knowledge and Personal Preferences

2017 ◽  
Vol 2623 (1) ◽  
pp. 82-89 ◽  
Author(s):  
Martin Stubenschrott ◽  
Thomas Matyus ◽  
Helmut Schrom-Feiertag ◽  
Christian Kogler ◽  
Stefan Seer
Keyword(s):  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
Behavioral Model ◽  
Emergency Evacuation ◽  
Global Knowledge ◽  
Evacuation Simulation ◽  
Route Choice Behavior ◽  
Crowd Density ◽  
The Impact

In recent years, pedestrian simulation has been a valuable tool for the quantitative assessment of egress performance in various environments during emergency evacuation. For a high level of realism, an evacuation simulation requires a behavioral model that takes into account behavioral aspects of real pedestrians. In many studies, however, it is assumed that simulated pedestrians have a global knowledge of the infrastructure and choose either a predefined or the shortest route. It is questionable whether this simplification provides realistic results. This study addresses the problem of human-like route-choice behavior for microscopic pedestrian simulations. A route-choice model is presented that considers two concepts: first, the modeling of infrastructure knowledge to represent the variations in the decision-making processes of pedestrians with different degrees of familiarity with the infrastructure (e.g., regular commuters versus first-time visitors). Second, for each pedestrian the internal preference for selecting a certain path can be calibrated to allow the choice for the fastest routes or the ones that are most convenient for the agent (e.g., by avoiding stairs). The approach here uses a hybrid route-choice behavior model composed of a graph-based macrolevel representation of the environment, which is augmented with local information to avoid obstacles and dense crowds in the vicinity. This method was applied with different parameter sets in an evacuation study of a multilevel subway station. The results show the impact of these parameters on evacuation times, use of infrastructure elements, and crowd density at specific locations.

GLO-SLAM: a slam system optimally combining GPS and LiDAR odometry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruihao Lin ◽  
Junzhe Xu ◽  
Jianhua Zhang
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Gps Data ◽  
Optimization Strategy ◽  
Data Set ◽  
Content Type ◽  
Localization And Mapping ◽  
Robot Positioning

Purpose Large-scale and precise three-dimensional (3D) map play an important role in autonomous driving and robot positioning. However, it is difficult to get accurate poses for mapping. On one hand, the global positioning system (GPS) data are not always reliable owing to multipath effect and poor satellite visibility in many urban environments. In another hand, the LiDAR-based odometry has accumulative errors. This paper aims to propose a novel simultaneous localization and mapping (SLAM) system to obtain large-scale and precise 3D map. Design/methodology/approach The proposed SLAM system optimally integrates the GPS data and a LiDAR odometry. In this system, two core algorithms are developed. To effectively verify reliability of the GPS data, VGL (the abbreviation of Verify GPS data with LiDAR data) algorithm is proposed and the points from LiDAR are used by the algorithm. To obtain accurate poses in GPS-denied areas, this paper proposes EG-LOAM algorithm, a LiDAR odometry with local optimization strategy to eliminate the accumulative errors by means of reliable GPS data. Findings On the KITTI data set and the customized outdoor data set, the system is able to generate high-precision 3D map in both GPS-denied areas and areas covered by GPS. Meanwhile, the VGL algorithm is proved to be able to verify reliability of the GPS data with confidence and the EG-LOAM outperform the state-of-the-art baselines. Originality/value A novel SLAM system is proposed to obtain large-scale and precise 3D map. To improve the robustness of the system, the VGL algorithm and the EG-LOAM are designed. The whole system as well as the two algorithms have a satisfactory performance in experiments.

scholarly journals Monitoring aerosol–cloud interactions at the CESAR Observatory in the Netherlands

2017 ◽  
Vol 10 (5) ◽  
pp. 1987-1997 ◽  
Author(s):  
Karolina Sarna ◽  
Herman W. J. Russchenberg
Keyword(s):  
Remote Sensing ◽  
The Netherlands ◽  
Climate Models ◽  
Cloud Droplet ◽  
Doppler Velocity ◽  
Backscatter Coefficient ◽  
Liquid Water Path ◽  
Data Set ◽  
Aerosol Cloud ◽  
The Impact

Abstract. The representation of aerosol–cloud interaction (ACI) processes in climate models, although long studied, still remains the source of high uncertainty. Very often there is a mismatch between the scale of observations used for ACI quantification and the ACI process itself. This can be mitigated by using the observations from ground-based remote sensing instruments. In this paper we presented a direct application of the aerosol–cloud interaction monitoring technique (ACI monitoring). ACI monitoring is based on the standardised Cloudnet data stream, which provides measurements from ground-based remote sensing instruments working in synergy. For the data set collected at the CESAR Observatory in the Netherlands we calculate ACI metrics. We specifically use attenuated backscatter coefficient (ATB) for the characterisation of the aerosol properties and cloud droplet effective radius (re) and number concentration (Nd) for the characterisation of the cloud properties. We calculate two metrics: ACIr  =  ln(re)/ln(ATB) and ACIN  =  ln(Nd)/ln(ATB). The calculated values of ACIr range from 0.001 to 0.085, which correspond to the values reported in previous studies. We also evaluated the impact of the vertical Doppler velocity and liquid water path (LWP) on ACI metrics. The values of ACIr were highest for LWP values between 60 and 105 g m−2. For higher LWP other processes, such as collision and coalescence, seem to be dominant and obscure the ACI processes. We also saw that the values of ACIr are higher when only data points located in the updraught regime are considered. The method presented in this study allow for monitoring ACI daily and further aggregating daily data into bigger data sets.

scholarly journals Can healthy checkout counters in supermarkets promote healthier food choices? Two real-life experiments in supermarkets in a disadvantaged urban area in the Netherlands

2020 ◽  
Author(s):  
Marlijn Huitink ◽  
Maartje P. Poelman ◽  
Jacob C. Seidell ◽  
Lothar D. J. Kuijper ◽  
Trynke Hoekstra ◽  
...  
Keyword(s):  
The Netherlands ◽  
Urban Area ◽  
Control Period ◽  
Experimental Studies ◽  
Real Life ◽  
Future Research ◽  
Intervention Period ◽  
Quasi Experimental ◽  
The Impact ◽  
Unhealthy Snacks

Abstract Background Most foods displayed at supermarket checkouts are unhealthy and do not support healthy purchases. This study investigates the sales effects of introducing healthier alternatives at supermarket checkouts. Methods We performed two real-life quasi-experimental studies in supermarkets located in a disadvantaged urban area in the Netherlands. In Study 1, we examined the impact of substituting healthier options for all the unhealthy snacks at checkouts (n = 1 supermarket). In Study 2, we investigated the impact of placing healthier snacks at checkouts (placement intervention), as well as the impact of offering a discount on healthier checkout snacks (placement + price intervention), while continuing to display unhealthy snacks for sale (n = 2 supermarkets). Supermarket sales data were used to measure purchases. Results In Study 1, median weekly sales/1000 customers of checkout snacks were 2.3 times lower (SE: 1.1, 95% CI: 1.9–2.7) during the intervention period – when healthier options were substituted for the entire unhealthy snack assortment – (median: 10, IQR: 2.8), as compared to the control period (median: 24, IQR: 2.8). In Study 2, median daily sales/1000 customers of healthier snacks were 2.1 times higher (SE: 1.3, 95% CI: 1.3–3.3) during the placement-intervention period (median: 7.8, IQR: 4.6), as compared to the control period (median: 4.2, IQR: 4.6). Similarly, median daily sales/1000 customers of healthier snacks were 2.7 times higher (SE: 1.2, 95%CI: 2.0–3.6) during the placement + price-intervention period (median: 5.8, IQR: 2.2), as compared to the control period (median: 2.2, IQR: 4.7). There was no difference between the effect of the placement intervention and that of the placement + price intervention (ratio: 1.1, SE: 1.3, 95% CI: 1.7–1.9). Neither did we observe a decline in purchases of unhealthy snacks (ratio: 1.3, SE: 1.1, 95% CI: 1.1–1.5). Conclusions This study showed that if we want to promote healthier food purchases at supermarket checkouts the substitution of the unhealthy snacks with healthier alternatives is an effective intervention, which is not the case if the unhealthy snacks remain in place at the checkouts, even with discounts on the healthier snacks. Future research should assess the feasibility and willingness of eliminating unhealthy checkout snacks in supermarkets.

scholarly journals Dynamic O-D Demand Estimation: Application of SPSA AD-PI Method in Conjunction with Different Assignment Strategies

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Marialisa Nigro ◽  
Akmal Abdelfatah ◽  
Ernesto Cipriani ◽  
Chiara Colombaroni ◽  
Gaetano Fusco ◽  
...  
Keyword(s):  
Traffic Assignment ◽  
Polynomial Interpolation ◽  
Dynamic Traffic Assignment ◽  
Choice Model ◽  
Route Choice ◽  
Demand Estimation ◽  
User Equilibrium ◽  
Dynamic Traffic ◽  
Dynamic Demand ◽  
The Impact

This paper examines the impact of applying dynamic traffic assignment (DTA) and quasi-dynamic traffic assignment (QDTA) models, which apply different route choice approaches (shortest paths based on current travel times, User Equilibrium: UE, and system optimum: SO), on the accuracy of the solution of the offline dynamic demand estimation problem. The evaluation scheme is based on the adoption of a bilevel approach, where the upper level consists of the adjustment of a starting demand using traffic measures and the lower level of the solution of the traffic network assignment problem. The SPSA AD-PI (Simultaneous Perturbation Stochastic Approximation Asymmetric Design Polynomial Interpolation) is adopted as a solution algorithm. A comparative analysis is conducted on a test network and the results highlight the importance of route choice model and information for the stability and the quality of the offline dynamic demand estimations.

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