Green walking networks for climate change adaptation

Climate change (CC) potentially affects people travel behaviour, due to extreme weather conditions. This is particularly true for pedestrians, that are more exposed to weather conditions. Introducing the effect of this change in transport modelling allows to analyse and plan walking networks taking into consideration the climatic variable. The aim of this work is to develop a tool that can support planning and design of walking networks, by assessing the effects of actions oriented to increase resilience with respect to extreme weather conditions (CC adaptation).An integrated approach is used, thus combining transport and land-use planning concepts with elements of outdoor thermal comfort and network accessibility. Walking networks are analysed through centrality indexes, including thermal comfort aspects into a general cost function of links and weighted nodes. The method has been applied to the walking network inside the Campus of the University of Catania (Italy), which includes different functions and where pedestrian paths are barely used by people. Results confirm that this tool is sensitive to the variables representing weather conditions and it can measure the influence of CC adaptation measures (e.g. vegetation) on walking attitude and on the performance of the walking network.     

Smarter and more connected: Future intelligent transportation system

Emerging technologies toward a connected vehicle-infrastructure-pedestrian environment and big data have made it easier and cheaper to collect, store, analyze, use, and disseminate multi-source data. The connected environment also introduces new approaches to flexible control and management of transportation systems in real time to improve overall system performance. Given the benefits of a connected environment, it is crucial that we understand how the current intelligent transportation system could be adapted to the connected environment.     

Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)

Studies of urban travel behaviour typically focus on weekday activities and commuting. This is surprising given the rising contribution of discretionary activities to daily travel that has occurred during the last few decades. Moreover, current understanding of the relationship between travel behaviour and land use remains incomplete, with little research carried out to explore spatial properties of activity-travel behaviour during the off-peak and weekend time periods. Weekend behaviours, for example, influenced by the availability of time and the spatiotemporal distribution of “weekend” destinations, likely produce spatially and temporally distinct activity-travel patterns. Using data from the first wave of the Toronto Travel-Activity Panel Survey (TTAPS), this paper examines an area of research that has received little attention; namely, the presence of spatial variety in activity-travel behaviour. The paper begins by looking at the extent to which individuals engage in spatially repetitive location choices during the course of a single week. Area-based measures of geographical extent and activity dispersion are then used to expose differences in weekday-to-weekend and day-to-day activity-travel patterns. Examination of unclassified activities carried out over a 1 week period reveals a level of spatial repetition that does not materialise across activities classified by type, travel mode, and planning strategy. Despite the inherent spatial flexibility offered by the personal automobile, spatial repetition is also found to be surprisingly similar across travel modes. The results also indicate weekday-to-weekend, and day-to-day fluctuations in spatial properties of individual activity-travel behaviour. These findings challenge the utility of the short-run survey as an instrument for capturing archetypal patterns of spatial behaviour. In addition, the presence of a weekday-to-weekend differential in spatial behaviour suggests that policies targeting weekday travel reduction could have little impact on travel associated with weekend activities.

考虑匹配优先级的共享混合停车位租用与分配问题研究

为充分利用混合停车场的停车和充电资源，本文依据出行者需求的差异性设置匹配优先级，在此基础上以共享平台接受并出租车位的收益扣除拒绝请求的惩罚成本的总收益最大化为目标，以充电量不超过停车场负荷为约束条件，建立考虑匹配优先级的共享混合停车位租用与分 配(MPRA)模型。基于匹配优先级原则和模型特征改进蚁群算法，设置分块冲突矩阵，根据分块冲突矩阵设计蚂蚁信息素更新策略和路径选择策略并进行求解。最后算例证实了考虑匹配优先级的必要性，并分析了问题规模对MPRA分配方案的影响。研究结果表明：在充电需求匹配率方面，MPRA方案高于利润最大化方案，平均提升率为28.96%，这一优势与出行者数量或车位数量成反比，且利润提升率随着问题规模的增加而增加；车位数量有限时，MPRA方案相较于先到先停方案可以极大化提升车位利用率和充电需求匹配率，平均提升率分别为17.87%和113.96%。     

线性空间中集值优化问题弱有效解与向量鞍点

在序线性空间中定义了带广义不等式约束集值优化问题的广义向量Fritz-John鞍点和广义向量Kuhn-Tucker鞍点,建立了二者之间关系.最后,借助广义锥次似凸映射的择一定理,讨论了集值优化问题的弱有效解与它们之间的关系.     

具可数基的Banach空间到其万有空间的细小内同构

本文证明了对任意具可数基的Banach空间E,对任意自然数北,都存在E的同胚空间Et和l/k内同构TkcB〔E、,F〕,使对任意等距算子TCB〔E、,F〕,都成立不等式∣∣Tk一T∣∣》1这里F为可数基空间的万有空间L∞,L∞,或C(0.1〕。      

凸度量空间中渐近拟非扩张映象具误差的Ishikawa型迭代序列的收敛性

笔者在凸度量空间中,对渐近非扩张映象T证明了带误差的Ishikawa型迭代序列收敛于不动点的一个充要条件,其中T不必是连续的.     

序线性空间中集值优化问题弱有效解的最优性条件

在序线性空间中,引入(u,02;Y+)—广义次似凸集值映射,建立了此映射下的一个择一定理.利用此定理,在序线性空间中获得了带广义不等式约束的集值优化问题弱有效解的最优性必要条件和充分条件.     

序线性空间中集值优化问题弱有效解的最优性条件

在序线性空间中,引入(u,02;Y )—广义次似凸集值映射,建立了此映射下的一个择一定理.利用此定理,在序线性空间中获得了带广义不等式约束的集值优化问题弱有效解的最优性必要条件和充分条件.     

任意Banach空间强伪压缩映射不动点的迭代逼近

利用正规对偶映射的定义，给出了任意Banach空间Lipschitz强伪压缩映射不动点的Ishikawa迭代收敛定理。该定理不仅推广了已知结果，而且还简化了目前相应结果的证明。