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Although multimodality has been widely studied in the literature, planning and operating bus lanes in congested urban city centers are still challenging topics for researchers and policy makers. Most existing approaches lack quantitative methods for estimating the impact of bus lanes or for optimizing the operation of bus lanes at a system level. This paper proposes a novel optimization approach for allocating road space to bus lanes in cities. The approach determines the optimal space share between the modes in service and allocates the bus lanes by integrating strategies that lead to less total travel cost. By relying on recent advances in network-level traffic flow modeling, namely, the multimodal macroscopic fundamental diagram (mMFD), the approach captures multimodal traffic dynamics and travel costs by mode. The impact of a bus lane on mode usage is taken into account to aggregated mode shift phenomena under changes in layout of dedicated bus lanes. Simulation was performed in a Swiss city network to test the proposed optimization approach. The research found that ( a) the mMFD could be properly integrated to decide for road space optimization of large-scale multimodal urban networks, ( b) an optimal and efficient space share minimized the total travel cost for all users, and ( c) the best strategy for the studied network was to implement the allocated space on the connected links on a corridor rather than to assign them sparsely to the links that are heavily congested.

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Unlike the lockdown measures taken in some countries or cities, the Japanese government declared a "State of Emergency" (SOE) under which people were only requested to reduce their contact with other people by at least 70 %, while some local governments also implemented their own mobility-reduction measures that had no legal basis. The effects of these measures are still unclear. Thus, in this study, we investigate changes in travel patterns in response to the COVID-19 outbreak and related policy measures in Japan using longitudinal aggregated mobile phone data. Specifically, we consider daily travel patterns as networks and analyze their structural changes by applying a framework for analyzing temporal networks used in network science. The cluster analysis with the network similarity measures across different dates showed that there are six main types of mobility patterns in the three major metropolitan areas of Japan: (I) weekends and holidays prior to the COVID-19 outbreak, (II) weekdays prior to the COVID-19 outbreak, (III) weekends and holidays before and after the SOE, (IV) weekdays before and after the SOE, (V) weekends and holidays during the SOE, and (VI) weekdays during the SOE. It was also found that travel patterns might have started to change from March 2020, when most schools were closed, and that the mobility patterns after the SOE returned to those prior to the SOE. Interestingly, we found that after the lifting of the SOE, travel patterns remained similar to those during the SOE for a few days, suggesting the possibility that self-restraint continued after the lifting of the SOE. Moreover, in the case of the Nagoya metropolitan area, we found that people voluntarily changed their travel patterns when the number of cases increased.

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