A fully separated bicycle network from vehicular traffic is not realistic even for the most bicycle-friendly cities. Thus, all around the world urban cycling entails switching between streets of different safety, convenience, and comfort levels. As a consequence, the quality of bicycle networks should be evaluated not based on one but multiple factors and by considering the different user preferences regarding these factors. More comprehensive methodologies to assess urban bicycle networks are essential to the operation and planning of modern city transportation. This work proposes a multi-objective methodology to assess—what we refer to as—bikeability between origin–destination locations and over the entire network, useful for evaluation and planning of bicycle networks. We do so by introducing the concept of bikeability curves which allows us to assess the quality of cycling in a city network with respect to the heterogeneity of user preferences. The application of the proposed methodology is demonstrated on two cities with different bike cultures: Amsterdam and Melbourne. Our results suggest the effectiveness of bikeability curves in describing the characteristic features and differences in the two networks.@en

Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best.

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This paper describes a method to estimate the parameters of a Recursive link-based Logit model (RL) using measurements of a set of spatially fixed proximity sensors, with limited hit rates, which can uniquely identify people, such as Wi-Fi-, RFID- or Bluetooth-sensors. The observed ‘route’ of an individual, where we focus on pedestrians in an urban or event context, is modelled as the sequence of sensors that have identified the individual during his or her trip. Obviously, these ‘routes’ contain large gaps, which makes traditional estimation techniques not applicable. Although we do not exactly know what happens within these gaps, we do have some specific insight about the individuals behavior between two identifications; we know with a certain probability which is related to the hit rate of the sensors, that the individual did not cross another sensor location between the two identifications. This paper therefore describes a method to estimate the parameters of an RL model that specifically exploits this knowledge. The framework also allows us to formulate a probabilistic link utilization estimation method, which can be used to estimate link flows in a network based on the sensor observations. The effectiveness of the methodology is demonstrated in simulation using an artificial network, after which the methodology is tested on a real data set, collected at a Dutch music event.

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We have experimented with and simulated Steinkamp's passive-dynamic hopper. This hopper cannot stand up (it is statically unstable), yet it can hop the length of a 5m 0.079 rad sloped ramp, with n ∼100 hops. Because, for an unstable periodic motion, a perturbation δx₀ grows exponentially with the number of steps (δx_n ∼ δx₀ × λⁿ), where λ is the system eigenvalue with largest magnitude, one expects that if λ > 1 that the amplification after 100 steps, λ¹⁰⁰, would be large enough to cause robot failure. So, the experiments seem to indicate that the largest eigenvalue magnitude of the linearized return map is less than one, and the hopper is dynamically stable. However, two independent simulations show more subtlety. Both simulations correctly predict the period of the basic motion, the kinematic details, and the existence of the experimentally observed period ∼11 solutions. However, both simulations also predict that the hopper is slightly unstable (λ_max > 1). This theoretically predicted instability superficially contradicts the experimental observation of 100 hops. Nor do the simulations suggest a stable attractor near the periodic motion. Instead, the conflict between the linearized stability analysis and the experiments seems to be resolved by the details of the launch: a simulation of the hand-holding during launch suggests that experienced launchers use the stability of the loosely held hopper to find a motion that is almost on the barely unstable limit cycle of the free device.

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