Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto

Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.     

Understanding the impacts of a combination of service improvement strategies on bus running time and passenger’s perception

Transit agencies implement many strategies in order to provide an attractive transportation service. This article aims to evaluate the impacts of implementing a combination of strategies, designed to improve the bus transit service, on running time and passenger satisfaction. These strategies include using smart card fare collection, introducing limited-stop bus service, implementing reserved bus lanes, using articulated buses, and implementing transit signal priority (TSP). This study uses stop-level data collected from the Société de transport de Montréal (STM)’s automatic vehicle location (AVL) and automatic passenger count (APC) systems, in Montréal, Canada. The combination of these strategies has lead to a 10.5% decline in running time along the limited stop service compared to the regular service. The regular route running time has increased by 1% on average compared to the initial time period. The study also shows that riders are generally satisfied with the service improvements. They tend to overestimate the savings associated with the implementation of this combination of strategies by 3.5-6.0 min and by 2.5-4.1 min for both the regular route and the limited stop service, respectively. This study helps transit planners and policy makers to better understand the effects of implementing a combination of strategies to improve running time and passenger’s perception of these changes in service.     

On the use of mathematical programming in the evaluation of transport policies

This paper focuses on the evaluation processes by which decisions regarding transportation alternatives can be assisted. A multidimensional approach usually called multiple criteria decision making is required to represent the complexity of transportation policy and systems.

The multiple criteria decision making techniques can be divided into two groups. The first is based on a ranking scheme approach and the second on a mathematical programming approach.

A multiple objective mathematical programming procedure known as Goal Programming is presented. The authors examined the use of that procedure in real transportation problems.

The results suggest that multiple objective mathematical programming techniques in general do not appear to be appropriate in transportation policy analysis involving mutually exclusive alternatives. Their use can be limited to special cases in the private sector.     

Dynamic response of offshore jacket structures under random loads

In this paper, the dynamic response of a scale model of a jacket offshore structure is investigated both theoretically and experimentally. The experiments were conducted both in air and in water. The in-water experiments were done in the towing tank of Memorial University to simulate the realistic operating conditions. The model was subjected to random wave loads. Froude's law of modeling was used to obtain the dimensions of the scale model based on the dimensions of an existing structure. The effects of varying the structure's weight, and the characteristics of the wave loading were investigated. The structure's weight was changed by adding weights to the structure's deck. A finite element model was designed to determine the dynamic response of the model. Excellent agreement between the experimental and theoretical results was obtained.The reaction force at the foundation was estimated from strain measurements and compared with the finite element calculations. Fair agreement was obtained.This work is the first stage of a project whose objective is to develop a method for structural damage detection using the free vibration response of the structure. The free vibration response will be obtained from the stationary random response of the structure using the random decrement method. Having an accurate model to describe the dynamic response of the structure is the first step in this study.     

Damage detection in offshore structures using neural networks

This paper discusses the damage detection in offshore jacket platforms subjected to random loads using a combined method of random decrement signature and neural networks. The random decrement technique is used to extract the free decay of the structure from its online response while the structure is in service. The free decay and its time derivative are used as input for a neural network. The output of the neural network is used as an index for damage detection. It has been shown that function N is effective in damage detection in the members of an offshore structure. Experimental studies conducted on a reduced model for a real jacket structure with geometrical scale of 1:30 are used. The applied loads were random loads. Two different load spectra were used: White noise, and Pierson-Moskowitz.     

Large-scale application of MILATRAS: case study of the Toronto transit network

This paper documents the efforts to operationalize the conceptual framework of MIcrosimulation Learning-based Approach to TRansit Assignment (MILATRAS) and its component models of departure time and path choices. It presents a large-scale real-world application, namely the multi-modal transit network of Toronto which is operated by the Toronto Transit Commission (TTC). This large-scale network is represented by over 500 branches with more than 10,000 stops. About 332,000 passenger-agents are modelled to represent the demand for the TTC in the AM peak period. A learning-based departure time and path choice model was adopted using the concept of mental models for the modelling of the transit assignment problem. The choice model parameters were calibrated such that the entropy of the simulated route loads was optimized with reference to the observed route loads, and validated with individual choices. A Parallel Genetic Algorithm engine was used for the parameter calibration process. The modelled route loads, based on the calibrated parameters, greatly approximate the distribution underlying the observed loads. 75% of the exact sequence of transfer point choices were correctly predicted by the off-stop/on-stop choice mechanism. The model predictability of the exact sequence of route transfers was about 60%. In this application, transit passengers were assumed to plan their transit trip based on their experience with the transportation network; with no prior (or perfect) knowledge of service performance.     

Trace metals in water, sediments and marine organisms from the northern part of the Gulf of Suez, Red Sea

Concentrations of Cd, Pb, Cu and Zn were determined in water, sediments, gastropod (Bulla umpulla) and green algae (Ulva lactuca) collected from five stations in the western side of the northern part of the Gulf of Suez during the period February 1993–January 1994. Sediments recorded the highest concentrations of Cd (2.26–4.40 μg/g) and Pb (13.90–28.34 μg/g), While the highest concentrations of the essential metals Cu and Zn were found in B. umpulla (28.19–72.04 and 60.24–108.74 μg/g, respectively). Water and sediments showed similar spatial distribution patterns for the highest mean values of the different metals. Highest values of the studied metals were found at stations influenced by various pollution sources such as harbours, and sewage and industrial drains. In contrast, the lowest concentrations were observed faraway from any pollution source. Calculations of concentration factors (C.F.) for gastropod and algae showed highest C.F. of Cd (4312.5–8705.9) and Pb (2103.3–8317.9) in algae, and highest C.F. of Cu (5288.9–42376.5) and Zn (3686.7–9631.5) in gastropod.     

Suboptimal Control Design of Active and Passive Suspensions Based on a Full Car Model

An optimal control design method is introduced and then applied to the optimum design of active and passive suspension systems. A basic three-dimensional 7-DOF car riding model subjected to four correlated random road inputs is considered. The design method is basically developed to allow arbitrary choice of sensors for various car state variables to be used for feedback control of each suspension unit. Previous studies show that full-state control laws and even some limited-state control laws often include feedback gains which are almost zero. Some other gains, although not zero, don't play an important role in improving the system performance measures. With the method proposed in this work, every suspension unit can have its own feedback measurements and the criterion function can be related to all state and control variables. Thus a large number of active and semi-active suspension systems with full- or limited-state control laws based on different measurement combination can be suggested, studied, and compared with each other. Instead of comparing these optimized active and semi-active suspension systems with a basic, passive suspension, the passive system itself is optimized with the same criterion. Simulations in the time domain and frequency analyses are performed, and comparisons are made among the systems in terms of r.m.s. car response measures and ISO riding comfort criterion.     

Stochastic Optimal Control of Highway Tractors with Active Suspensions

Stochastic optimal control and estimation theories are used to design an active suspension system for a cab ride in a tractor-semitrailer vehicle. A discrete-continuous vehicle model with eleven degrees of freedom is augmented by a stochastic road excitation model and a human perception of vibration shape filter. Both perfect measurement and estimated state cases are considered. The impact of the measurement noise on the design of the optimal controller is demonstrated. The performance of the optimally controlled system is compared with an optimal passive system. It is shown that significant improvements in ride comfort can be achieved through the use of actively controlled cab suspensions.     

Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes

Transportation - Activity sequencing is a crucial component of disaggregate modeling approaches. This paper presents a methodology to analyse and predict activity sequence patterns for persons...