The increasing adoption of electric vehicles poses significant challenges for charging infrastructure planning and grid integration under dynamic traffic conditions. This study presents a traffic informed EV charging demand estimation framework for EV charging infrastructure planning by integrating observed traffic flow data, EV charging demand estimation, energy demand modeling, and G/G/C queueing analysis. A case study is conducted on the Huron Church Road-Highway 401 corridor in Windsor, Ontario, Canada, using lane level hourly traffic data collected across seven lanes. EV charging demand is estimated using an EV penetration rate of 3.4% and a charging station capture rate of 4%, while charging load is calculated based on an average energy consumption of 50 kWh per EV. The results estimate a current charging demand of approximately 48 EVs/day, with a peak arrival rate of 3.25 EVs/hr, corresponding to a peak traffic flow of 2,389 vehicles/hr and a peak charging load of 162.5 kWh/hr (0.163 MW). Scenario analysis shows that under higher EV penetration levels, charging demand may increase to approximately 706 EV/day, with peak load reaching approximately 2.39 MW. Queueing analysis demonstrates that charger configuration significantly affects system performance. Compared with a two-charger configuration, a three-charger system reduces utilization from 0.475 to 0.317 and decreases average waiting time to approximately 5.8 minutes under the Kramer Langenbach Belz (KLB) approximation. The proposed framework provides an integrated transportation energy approach for scalable EV charging infrastructure planning, supporting grid operators, policymakers, and infrastructure planners under future electrification scenarios.